{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interpreting nodes and edges with saliency maps in GCN (sparse)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gcn-sparse-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gcn-sparse-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This demo shows how to use integrated gradients in graph convolutional networks to obtain accurate importance estimations for both the nodes and edges. The notebook consists of three parts:\n",
    "- setting up the node classification problem for Cora citation network\n",
    "- training and evaluating a GCN model for node classification\n",
    "- calculating node and edge importances for model's predictions of query (\"target\") nodes\n",
    "\n",
    "<a name=\"refs\"></a>\n",
    "**References**\n",
    "\n",
    "[1] Axiomatic Attribution for Deep Networks. M. Sundararajan, A. Taly, and Q. Yan.\n",
    "    Proceedings of the 34th International Conference on Machine Learning, Sydney, Australia, PMLR 70, 2017\n",
    "    ([link](https://arxiv.org/pdf/1703.01365.pdf)).\n",
    "    \n",
    "[2] Adversarial Examples on Graph Data: Deep Insights into Attack and Defense. H. Wu, C. Wang, Y. Tyshetskiy, A. Docherty, K. Lu, and L. Zhu. arXiv: 1903.01610 ([link](https://arxiv.org/abs/1903.01610)).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "outputs": [],
   "source": [
    "# install StellarGraph if running on Google Colab\n",
    "import sys\n",
    "if 'google.colab' in sys.modules:\n",
    "  %pip install -q stellargraph[demos]==1.2.1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "VersionCheck"
    ]
   },
   "outputs": [],
   "source": [
    "# verify that we're using the correct version of StellarGraph for this notebook\n",
    "import stellargraph as sg\n",
    "\n",
    "try:\n",
    "    sg.utils.validate_notebook_version(\"1.2.1\")\n",
    "except AttributeError:\n",
    "    raise ValueError(\n",
    "        f\"This notebook requires StellarGraph version 1.2.1, but a different version {sg.__version__} is installed.  Please see <https://github.com/stellargraph/stellargraph/issues/1172>.\"\n",
    "    ) from None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import os\n",
    "import time\n",
    "import stellargraph as sg\n",
    "from stellargraph.mapper import FullBatchNodeGenerator\n",
    "from stellargraph.layer import GCN\n",
    "from tensorflow import keras\n",
    "from tensorflow.keras import layers, optimizers, losses, metrics, Model, regularizers\n",
    "from sklearn import preprocessing, feature_extraction, model_selection\n",
    "from copy import deepcopy\n",
    "import matplotlib.pyplot as plt\n",
    "from stellargraph import datasets\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the CORA network"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": [
     "DataLoadingLinks"
    ]
   },
   "source": [
    "(See [the \"Loading from Pandas\" demo](../basics/loading-pandas.ipynb) for details on how data can be loaded.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "DataLoading"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "The Cora dataset consists of 2708 scientific publications classified into one of seven classes. The citation network consists of 5429 links. Each publication in the dataset is described by a 0/1-valued word vector indicating the absence/presence of the corresponding word from the dictionary. The dictionary consists of 1433 unique words."
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dataset = datasets.Cora()\n",
    "display(HTML(dataset.description))\n",
    "G, subjects = dataset.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Splitting the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For machine learning we want to take a subset of the nodes for training, and use the rest for validation and testing. We'll use scikit-learn again to do this.\n",
    "\n",
    "Here we're taking 140 node labels for training, 500 for validation, and the rest for testing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_subjects, test_subjects = model_selection.train_test_split(\n",
    "    subjects, train_size=140, test_size=None, stratify=subjects\n",
    ")\n",
    "val_subjects, test_subjects = model_selection.train_test_split(\n",
    "    test_subjects, train_size=500, test_size=None, stratify=test_subjects\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting to numeric arrays"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For our categorical target, we will use one-hot vectors that will be fed into a soft-max Keras layer during training. To do this conversion ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "target_encoding = preprocessing.LabelBinarizer()\n",
    "\n",
    "train_targets = target_encoding.fit_transform(train_subjects)\n",
    "val_targets = target_encoding.transform(val_subjects)\n",
    "test_targets = target_encoding.transform(test_subjects)\n",
    "\n",
    "all_targets = target_encoding.transform(subjects)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the GCN model in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To feed data from the graph to the Keras model we need a generator. Since GCN is a full-batch model, we use the `FullBatchNodeGenerator` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using GCN (local pooling) filters...\n"
     ]
    }
   ],
   "source": [
    "generator = FullBatchNodeGenerator(G, sparse=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For training we map only the training nodes returned from our splitter and the target values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_gen = generator.flow(train_subjects.index, train_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can specify our machine learning model: tn this example we use two GCN layers with 16-dimensional hidden node features at each layer with ELU activation functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "layer_sizes = [16, 16]\n",
    "gcn = GCN(\n",
    "    layer_sizes=layer_sizes,\n",
    "    activations=[\"elu\", \"elu\"],\n",
    "    generator=generator,\n",
    "    dropout=0.3,\n",
    "    kernel_regularizer=regularizers.l2(5e-4),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Expose the input and output tensors of the GCN model for node prediction, via GCN.in_out_tensors() method:\n",
    "x_inp, x_out = gcn.in_out_tensors()\n",
    "# Snap the final estimator layer to x_out\n",
    "x_out = layers.Dense(units=train_targets.shape[1], activation=\"softmax\")(x_out)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training the model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's create the actual Keras model with the input tensors `x_inp` and output tensors being the predictions `x_out` from the final dense layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Model(inputs=x_inp, outputs=x_out)\n",
    "\n",
    "model.compile(\n",
    "    optimizer=optimizers.Adam(lr=0.01),  # decay=0.001),\n",
    "    loss=losses.categorical_crossentropy,\n",
    "    metrics=[metrics.categorical_accuracy],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model, keeping track of its loss and accuracy on the training set, and its generalisation performance on the validation set (we need to create another generator over the validation data for this)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_gen = generator.flow(val_subjects.index, val_targets)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Train the model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "1/1 - 0s - loss: 2.0886 - categorical_accuracy: 0.0786 - val_loss: 1.8735 - val_categorical_accuracy: 0.2860\n",
      "Epoch 2/20\n",
      "1/1 - 0s - loss: 1.8273 - categorical_accuracy: 0.3071 - val_loss: 1.7735 - val_categorical_accuracy: 0.3080\n",
      "Epoch 3/20\n",
      "1/1 - 0s - loss: 1.6816 - categorical_accuracy: 0.3429 - val_loss: 1.7049 - val_categorical_accuracy: 0.3280\n",
      "Epoch 4/20\n",
      "1/1 - 0s - loss: 1.5697 - categorical_accuracy: 0.3714 - val_loss: 1.6350 - val_categorical_accuracy: 0.4240\n",
      "Epoch 5/20\n",
      "1/1 - 0s - loss: 1.4508 - categorical_accuracy: 0.5000 - val_loss: 1.5633 - val_categorical_accuracy: 0.4860\n",
      "Epoch 6/20\n",
      "1/1 - 0s - loss: 1.3410 - categorical_accuracy: 0.5929 - val_loss: 1.4933 - val_categorical_accuracy: 0.5100\n",
      "Epoch 7/20\n",
      "1/1 - 0s - loss: 1.2154 - categorical_accuracy: 0.6714 - val_loss: 1.4239 - val_categorical_accuracy: 0.5420\n",
      "Epoch 8/20\n",
      "1/1 - 0s - loss: 1.1221 - categorical_accuracy: 0.6714 - val_loss: 1.3527 - val_categorical_accuracy: 0.5540\n",
      "Epoch 9/20\n",
      "1/1 - 0s - loss: 1.0248 - categorical_accuracy: 0.7286 - val_loss: 1.2816 - val_categorical_accuracy: 0.5820\n",
      "Epoch 10/20\n",
      "1/1 - 0s - loss: 0.9370 - categorical_accuracy: 0.7429 - val_loss: 1.2150 - val_categorical_accuracy: 0.6100\n",
      "Epoch 11/20\n",
      "1/1 - 0s - loss: 0.8205 - categorical_accuracy: 0.7929 - val_loss: 1.1561 - val_categorical_accuracy: 0.6420\n",
      "Epoch 12/20\n",
      "1/1 - 0s - loss: 0.7672 - categorical_accuracy: 0.8214 - val_loss: 1.1058 - val_categorical_accuracy: 0.6840\n",
      "Epoch 13/20\n",
      "1/1 - 0s - loss: 0.6830 - categorical_accuracy: 0.8500 - val_loss: 1.0636 - val_categorical_accuracy: 0.7120\n",
      "Epoch 14/20\n",
      "1/1 - 0s - loss: 0.6202 - categorical_accuracy: 0.8786 - val_loss: 1.0272 - val_categorical_accuracy: 0.7220\n",
      "Epoch 15/20\n",
      "1/1 - 0s - loss: 0.5606 - categorical_accuracy: 0.9143 - val_loss: 0.9955 - val_categorical_accuracy: 0.7380\n",
      "Epoch 16/20\n",
      "1/1 - 0s - loss: 0.5297 - categorical_accuracy: 0.9071 - val_loss: 0.9688 - val_categorical_accuracy: 0.7560\n",
      "Epoch 17/20\n",
      "1/1 - 0s - loss: 0.4936 - categorical_accuracy: 0.9429 - val_loss: 0.9467 - val_categorical_accuracy: 0.7600\n",
      "Epoch 18/20\n",
      "1/1 - 0s - loss: 0.4496 - categorical_accuracy: 0.9571 - val_loss: 0.9290 - val_categorical_accuracy: 0.7740\n",
      "Epoch 19/20\n",
      "1/1 - 0s - loss: 0.4013 - categorical_accuracy: 0.9643 - val_loss: 0.9158 - val_categorical_accuracy: 0.7780\n",
      "Epoch 20/20\n",
      "1/1 - 0s - loss: 0.3808 - categorical_accuracy: 0.9786 - val_loss: 0.9063 - val_categorical_accuracy: 0.7820\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(\n",
    "    train_gen, shuffle=False, epochs=20, verbose=2, validation_data=val_gen\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sg.utils.plot_history(history)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Evaluate the trained model on the test set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Set Metrics:\n",
      "\tloss: 0.9140\n",
      "\tcategorical_accuracy: 0.7843\n"
     ]
    }
   ],
   "source": [
    "test_gen = generator.flow(test_subjects.index, test_targets)\n",
    "test_metrics = model.evaluate(test_gen)\n",
    "print(\"\\nTest Set Metrics:\")\n",
    "for name, val in zip(model.metrics_names, test_metrics):\n",
    "    print(\"\\t{}: {:0.4f}\".format(name, val))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Node and link importance via saliency maps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to understand why a selected node is predicted as a certain class we want to find the node feature importance, total node importance, and link importance for nodes and edges in the selected node's neighbourhood (ego-net). These importances give information about the effect of changes in the node's features and its neighbourhood on the prediction of the node, specifically:\n",
    "\n",
    "- **Node feature importance**: Given the selected node $t$ and the model's prediction $s(c)$ for class $c$. The feature importance can be calculated for each node $v$ in the selected node's ego-net where the importance of feature $f$ for node $v$ is the change predicted score $s(c)$ for the selected node when the feature $f$ of node $v$ is perturbed.\n",
    "- **Total node importance**: This is defined as the sum of the feature importances for node $v$ for all features. Nodes with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "- **Link importance**: This is defined as the change in the selected node's predicted score $s(c)$ if the link $e=(u, v)$ is removed from the graph. Links with high importance (positive or negative) affect the prediction for the selected node more than links with low importance. \n",
    "\n",
    "Node and link importances can be used to assess the role of nodes and links in model's predictions for the node(s) of interest (the selected node). For datasets like CORA-ML, the features and edges are binary, vanilla gradients may not perform well so we use integrated gradients [[1]](#refs) to compute them.\n",
    "\n",
    "Another interesting application of node and link importances is to identify model vulnerabilities to attacks via perturbing node features and graph structure (see [[2]](#refs))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To investigate these importances we use the StellarGraph `saliency_maps` routines:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "from stellargraph.interpretability.saliency_maps import IntegratedGradients"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Select the target node whose prediction is to be interpreted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "graph_nodes = list(G.nodes())\n",
    "target_nid = 1109199\n",
    "target_idx = graph_nodes.index(target_nid)\n",
    "y_true = all_targets[target_idx]  # true class of the target node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Selected node id: 1109199, \n",
      "True label: [0 1 0 0 0 0 0], \n",
      "Predicted scores: [0.02 0.77 0.   0.02 0.1  0.01 0.07]\n"
     ]
    }
   ],
   "source": [
    "all_gen = generator.flow(graph_nodes)\n",
    "y_pred = model.predict(all_gen)[0, target_idx]\n",
    "class_of_interest = np.argmax(y_pred)\n",
    "\n",
    "print(\n",
    "    \"Selected node id: {}, \\nTrue label: {}, \\nPredicted scores: {}\".format(\n",
    "        target_nid, y_true, y_pred.round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Get the node feature importance by using integrated gradients"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "int_grad_saliency = IntegratedGradients(model, train_gen)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the parameters of `get_node_importance` method, `X` and `A` are the feature and adjacency matrices, respectively. If `sparse` option is enabled, `A` will be the non-zero values of the adjacency matrix with `A_index` being the indices. `target_idx` is the node of interest, and `class_of_interest` is set as the predicted label of the node. `steps` indicates the number of steps used to approximate the integration in integrated gradients calculation. A larger value of `steps` gives better approximation, at the cost of higher computational overhead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "integrated_node_importance = int_grad_saliency.get_node_importance(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2708,)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "integrated_node_importance [0. 0. 0. ... 0. 0. 0.]\n",
      "integrate_node_importance.shape = (2708,)\n",
      "integrated self-importance of target node 1109199: 6.31\n"
     ]
    }
   ],
   "source": [
    "print(\"\\nintegrated_node_importance\", integrated_node_importance.round(2))\n",
    "print(\"integrate_node_importance.shape = {}\".format(integrated_node_importance.shape))\n",
    "print(\n",
    "    \"integrated self-importance of target node {}: {}\".format(\n",
    "        target_nid, integrated_node_importance[target_idx].round(2)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check that number of non-zero node importance values is less or equal the number of nodes in target node's K-hop ego net (where K is the number of GCN layers in the model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "G_ego = nx.ego_graph(G.to_networkx(), target_nid, radius=len(gcn.activations))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of nodes in the ego graph: 202\n",
      "Number of non-zero elements in integrated_node_importance: 202\n"
     ]
    }
   ],
   "source": [
    "print(\"Number of nodes in the ego graph: {}\".format(len(G_ego.nodes())))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrated_node_importance: {}\".format(\n",
    "        np.count_nonzero(integrated_node_importance)\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute the link importance using integrated gradients [[1]](#refs). Integrated gradients are obtained by accumulating the gradients along the path between the baseline (all-zero graph) and the state of the graph. They provide better sensitivity for the graphs with binary features and edges compared with the vanilla gradients. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "integrate_link_importance = int_grad_saliency.get_integrated_link_masks(\n",
    "    target_idx, class_of_interest, steps=50\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "integrate_link_importance.shape = (2708, 2708)\n",
      "Number of non-zero elements in integrate_link_importance: 210\n"
     ]
    }
   ],
   "source": [
    "integrate_link_importance_dense = np.array(integrate_link_importance.todense())\n",
    "print(\"integrate_link_importance.shape = {}\".format(integrate_link_importance.shape))\n",
    "print(\n",
    "    \"Number of non-zero elements in integrate_link_importance: {}\".format(\n",
    "        np.count_nonzero(integrate_link_importance.todense())\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now find the nodes that have the highest importance to the prediction of the selected node:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 10 most important links by integrated gradients are:\n",
      " [(1544, 1206), (1544, 1544), (1544, 163), (1206, 1206), (1206, 789), (1206, 1544), (1544, 566), (1206, 163), (566, 733), (566, 1544)]\n"
     ]
    }
   ],
   "source": [
    "sorted_indices = np.argsort(integrate_link_importance_dense.flatten())\n",
    "N = len(graph_nodes)\n",
    "integrated_link_importance_rank = [(k // N, k % N) for k in sorted_indices[::-1]]\n",
    "topk = 10\n",
    "# integrate_link_importance = integrate_link_importance_dense\n",
    "print(\n",
    "    \"Top {} most important links by integrated gradients are:\\n {}\".format(\n",
    "        topk, integrated_link_importance_rank[:topk]\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the labels as an attribute for the nodes in the graph. The labels are used to color the nodes in different classes.\n",
    "nx.set_node_attributes(G_ego, values={x[0]: {\"subject\": x[1]} for x in subjects.items()})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we plot the link and node importance (computed by integrated gradients) of the nodes within the ego graph of the target node. \n",
    "\n",
    "For nodes, the shape of the node indicates the positive/negative importance the node has. 'round' nodes have positive importance while 'diamond' nodes have negative importance. The size of the node indicates the value of the importance, e.g., a large diamond node has higher negative importance. \n",
    "\n",
    "For links, the color of the link indicates the positive/negative importance the link has. 'red' links have positive importance while 'blue' links have negative importance. The width of the link indicates the value of the importance, e.g., a thicker blue link has higher negative importance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10.918084517035823"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrated_node_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.208803627230227"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "integrate_link_importance.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "node_size_factor = 1e2\n",
    "link_width_factor = 2\n",
    "\n",
    "nodes = list(G_ego.nodes())\n",
    "colors = pd.DataFrame(\n",
    "    [v[1][\"subject\"] for v in G_ego.nodes(data=True)], index=nodes, columns=[\"subject\"]\n",
    ")\n",
    "colors = np.argmax(target_encoding.transform(colors), axis=1) + 1\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "pos = nx.spring_layout(G_ego)\n",
    "\n",
    "# Draw ego as large and red\n",
    "node_sizes = [integrated_node_importance[graph_nodes.index(k)] for k in nodes]\n",
    "node_shapes = [\"o\" if w > 0 else \"d\" for w in node_sizes]\n",
    "\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "node_size_scale = node_size_factor / np.max(node_sizes)\n",
    "for k in range(len(nodes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(nodes[k])\n",
    "        positive_node_sizes.append(node_size_scale * node_sizes[k])\n",
    "\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(nodes[k])\n",
    "        negative_node_sizes.append(node_size_scale * abs(node_sizes[k]))\n",
    "\n",
    "# Plot the ego network with the node importances\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_nid)]),\n",
    "    node_shape=\"*\",\n",
    "    node_color=[colors[nodes.index(target_nid)]],\n",
    "    cmap=cmap,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "\n",
    "# Draw the edges with the edge importances\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    integrate_link_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We then remove the node or edge in the ego graph one by one and check how the prediction changes. By doing so, we can obtain the ground truth importance of the nodes and edges. Comparing the following figure and the above one can show the effectiveness of integrated gradients as the importance approximations are relatively consistent with the ground truth."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "(X, _, A_index, A), _ = train_gen[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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sOyb8NyHOUXSOOWDAR1W7daFFIOXtG51CLT+dYwnolL9/I10riVYXHS5hwZD0WOto45EVnHMiCGrxOX3tUvqZL7RcQckwDMMwuoAJ/02Mc8wDPSL0dvEi80DvavSz/fEKaDJuZ3YSVLwGAbuAlsRql6II6TrXOj1Qq0qh7LFu7Fr0+V0igMxp6+sXScXyFE7fhYthGIZxRmLCf5PjHIuolOteJFqTfAd986hOjTfckbGUFUT6/KKi7e+Ej1Svr6WnnNbFen/Xk3pF4lV80pQvNE4XdJGUwbnlM6RMrGEYhnGGYcLfwDmWgbxIFz3uoQtvh6LFN5z1vqkfefI/X9GJsXxkNnwXct52cqaRphOJsh16/0RuGhG56Vn+XudjFh85LS0+kZ9/ZZ1nYhiGYRhVMeG/wZAsfyhZ3r3W1y1L+u1OtFq7+o53Yqi/2nrDj/3LyI986fNDz+1U5D/nO+Sutw+/OXQXpW50+UXnfWJY9rsXNTm2WlbK6Uw0+yXAB1GLTyijmq7a9XiDIVneIVn26g/Si/n5DcMwjNMAE/4bj4vTLr+vY574JvCR1nlgKNGj3hk61d33UwPFhdc+d+ELIx2JQqtoC4nObt2r7zROppGqMZ8b/P7nAv/S5Ng93Ytg//RH38s/X4t+3jgNex/83yevPPxtvzjKmZ/fMAzDOB0w4b/xuK4gmecDIDK41gLUaa2WOaBPhB4AydInWTqVnNuR7r5uglMLhwZvBqaBtqv8SJY/3PXUI1kfQZ+j/RyCQpWKSc6XwuwUDUXfv/KdZ9/5zMWv727rSh2atwipCV6cej13L+BcMZSVPZ0sPu6gfOvhB55SND+/YRiGcTphwn+D4SZwbgKHczmcWwD618Nz7ht8pUXow7lv4dxnOzh4Hq2g04ma/Jp42m41Hueun0pvuQjo7YSQq1p/XivX9EqWHsnyA+1epyFEZP/yoczXH3rW0SbOGSTc2zht3ptgI9vPoXjS8Glj8QHiTbm6XnlIslwpWXZ2+zqGYRjG5sCE/0ZHxVHRR//X9P1yjiWgiJMPjRRnbvGCp1OD59BqOu0KdgecAsbb2R1xB+W8lUN9HwNCAutSR5uPlSLAVcC/Sbaj1Ymq0Upib6qki7Hek05EtlMOGQDtHH3adefVRbhbEz+/3vMvAG/u+rUMwzCMTUH3mjcZnUNFRh6RfkSIJUOuwaXJifBHs4z1Az2IpDrm+3Zu2dc9H0gsGdn4ODlEpoAdwLF2ZgQsE0pYinQzyfez2/OTPzx57465+oeuMdUWmO1H+1O3cJ12/HWu4C0+rpvdeSXLbmDGTehCo/WBRIAe1M/ffWuPLjDSGZd7Rl56Hun69QzDMIxNgUX8TydU8Od89H/NFm3OUQAWBJf+Ms9OdVQQd667b9gZaX0HQXch4ve10OZ9dsG/XvHEBLnJ+3Z+po2xu8kQznV0QRJKdV7Hbf3ewgZq8el2zf4ngNvaGkHzPjTRuduiPyyEoYhzi7mDPf/hJjjV1WsahmEYmwYT/qcbzhW8cEp30YqScFlN+n0OX07/b361fU996eDz6G5C6wnEKsimgbEOJEQv+4ZeC0DL99iL2o3wHWu2KVapuO2MxSzlkD583oBPHO+uXUakr7e49A/AJ9oYI4Panrpfn18Xvz04t2ilQQ3DMIxuYFaf05XIJjOI2g+6nmiol2Ve5H8PfInnFG7V5Ny5elFQySLAW4APugkerTLwHCJjiMy3LHo6YflxbsXbLILYPV3Keiajol0aLjepi6/y++/aiXSLIH/J6/Q+Opf3uyDFblp8vIjuXz7U/9I2xlib+vy6uOgBVkryKgzDMAyjw2yEaKTRKs4VfVRa1rL0p3Ms3sZ1xXN5NAcMNRARFuAtg8X559UZeBoYaSvCrNahAiLtJs3mvAie68BY5YSSnkVvI+kmKZqL9scbanWqU2/qdbx/AOcWvOUn1VWLj1rR+vznqZXzJdZRuLtCXOea8lF+E/2GYRhGVzHhv4GQbIs7MGpDWAQG1qr0p3OsfJdz84Ir+utWnbuboAjsOvjg/tvqzk+7+7ZV4x+1/Iy2IVpz6Hcj7SPlnV1QqbDu8+9b6/YmfX3dXuxJm9H+9BPsXrX40O3SnSqke3BupsXzU2hJ15WuNuUS6fFWveU1sREZhmEYBib8Nwy+VndOsjy7pQGccz76X1H609eMbzd5NuGS5IElwckT7O6pJerdBMfOX3l4Gd2dqLc4OdVWd1+1ZpxELT+tnB+EbojIr7SRfFzsYhdktYd0ChXNy7Gfay4qJMsByXJ29eFIvZmbUrs5Kt7ik6ab3Xkj0T/b4vkZdLG3XPfYVtHdBE2O1yi/Nf8yDMMw1gwT/huM7NK3htuqcONc3i8AemPj7ADa69pa9XIUnWPubJ5I/TMvrp9wHERVrdeoYmimvLuvZDlPsvyzZKm/q6FR9XwbNp38My74xtbMvtwnZL9z0MA1E6dBtR2Dzgi++sKxp4n8j0zFsbWj3ncDt9R4Xm7ixn6cm49ZfLoj/PVzl2lD9Ov720KujM9hafQaaqVao5wcwzAMw4hjwn+D4CY4BvR+68FLP4VW7Blvq5RkJHwH3UE56iboai1w55j/Uf5ZbuLN9Sv+qLXB1RH/BSq7+z4deHFfcanRTqYztGr5ca6Yk57+Aumrd+eeSBP58k8f9PPTNd/41vyJ3wD+R/KlST/O2b1AyBfIOEd3xK6K/nRLJUg1At+H+vmbtiBJlp+uu0MgkvZR/sJa9uAwDMMwjHKsqs8Gwk14YaQRexW9Kt5OtWQJiMRzHyI93RYdzrEgclPfPRzgH+tV/NHqOT2+bGaycNIqPSlEhnzJz3/JuFx26fDAbEMBc+fyiATLT9NVfg4+lH0MGEajtHN+EdJaRHl9aLzbru6MzMd+rr1YEhk6Af8nKXItgryFmzibJ1I4l/PdebuzAGlP9KeIquk0//0SyZx30cOHH+05709rHNOPVkUywW8YhmGsO2IW0w2OCrBRVDy0lrCo46SAftag9KcIPbs40nOEs9LAfE27iFbOSdcURiruZLXxk/ruBxu2dYhsRcVdK+Iw2DNmERlpxUoSataXlK8U6ce5pdW/aw+QbNcR6a2ZGNpMDfry16Ydmmu9b2PVquaIkHbIIM7NeotPdxJ6tZSt+EVhs+e2V59fdwmKVb9LOn4G/dx1zN4kWX4U+KyboPX/CwzDMIy2OFvEvW6NrnUj3OOcu6xT451e1oXNiCbtTgPz3v7TWuOsNSz96Ry5o+xeFpybZHvNij9eOBWo1Q1YS3QGS0bYyVj2wq8RQpWfVj7vRTS5dxBYbOKacfJQkeC7cUp56jyKsZ9r7xSIjAKJiygRUo9ybg9RknBmA4p+raTUiuhX204fKuiTFmMSW6gudVL0D+5b2A38I/DGTo1pGIZhbC5M+HcByXJOwwl/jaIde0+h9eXHawrl2uOsSelPX6d9fieT8p9c2lvzWo2J/3k0YTkkYdbPE4jOLQAngO3NvAZ/bh4VwRn/76bFemKjqnZLeTZGusHa8AMVArraVmAoIZowrghyFXfJuXw3g3Mr3uLTDdE/ROuiv/WmXPpZ06o/SfdHFxT9QOeTd0W2fPv+Cwo49wngn1seJotI1v7fNwzD2KzYL4AO43+pfneoONexbZkStL74KTQCPt6SeI9Kf7ry0p+dxGm/17mn85+8l9f31BH2OTQZuXpVILWi9K/uIOhOQM9qBLf2ZJb9+CN1j004G11w9fkxOiXYhfZKhdYYObEDbyPn1esNMAZVbSapu7hmEOfmfHfe5EVPO6jod02Lfs0V6UOtbs1F4aPk3FziLoGOPeDn1dkSnfod3wbM7M4fmXQH5cVugm+2MeK/4tyUZGm9XK5hGIZx2mLCv8P4ZlXPe83Uzf/hk1e7k0CtAuMUKnzHW7KNOJfzC4C+rojP1cuw8Au8t/Db/EmqplVGo7C5OuJ/hnh3X/XtN7p4ac3yE3Yk1Ou/iEZ1O0MTDcIkyw2S5dpGR27oKF04xqPTUlUYB0tQgrAVIfU1Luslsvh03tcfif6FJs9LUytSX/vcXkIOStJ90e9Nj/8+du71iowish3NkTnR4M5NrfHSiJw1UFzopVOlZA3DMIzTDqvq0wXcBJ+DXwZ+OUQDg6e480m1IfKpQiEFTDctbpxbRCTjRfly2yIj8RIsifx279e4vOcztSr+aCUeTehVkZ002JRP2D3pf55GZBw4VWcSBUSOAzuBIy28jEIbCzkn0mYde+f+EpHPAp9q8IxGFhS9ZUnPtT47w9USel/ObVzGPRmcW+yKxUerDhWqfiaqnxd2h5r77ul3KXj5K78PupjQhU4nO/zqd6MXmG0rmT8abwDdpSni3BOLfuHoJkz8G4ZhbEZM+HcbFQVFn/QXLCL5jtoB9Doz3qYxjki+6eozGq3MI9JPC6U/JcsO4ITf8ahyCVZErinu5GjfMXYNI5Jc8UfFv6sp/mEKkS04N+V/nqlVaSY29jIiK01X6NHyowCDwFyz5ztHwQvilkXi9y987uLPDT1vEgjCs9YCrRfN5WicWgnfNaxDIqSO8/ohYM5353WNWHwky5OAx4AL3QTfrnHtIfQ701xJzMjP39xCVs+Tqp89tf0Um16E1L7mEDCALojbLxmrCdiDaL7B6iLXBL9hGMbmxqw+a4X66kMpzbS3AXX2/us1ptDqM1taqkCj4mrFe/8bWhj6vIZjVGnmVDo8+Ul2LgrO3c9F1a+hYm2l6muIuvuO+5+LaM+CRjr1nkItP83ao+LVbrpWFaka//7wVd9zE4SFTfXEXRXwiZacsuMGiBpsKdXPGUzy1YuQ+gJX9m7j5IrorUn5xO66bMsfP9//8/yqB0WR/sZFvy6y1cLUjOjX80KjrcreEvqd7Uej/K2VAa0cc9Bbeoo4d7zpxU3leNsR2Y2+hiPeDmgYhmEYgAn/qnS8Kk8c5/J+AZDyYqKzZR11/CnUmjLetH8/Kv0ZkhbrngE8ANzb2PAUnWPuqdzv/okf6a+aoByV7UwuYarPz/voZmMJwnpcEZhELT/NsOL/DAFLHciLaMc6VIt6uwHRccGXXjvar5aXBK7iLrmSL/V6odx4d16R9PH7dtwPXA58psoxIzQb6dfvUo9Pgm88uq07Gr3ey18oey4sCJx/vv2oue6sbUN3Fo63tXugdsLdXvBPe8HffMUjwzAM44zHrD7V+QfJcspN8FNdu0IkutJeeLiOJgiqGFv2UcVx1EbQ+PhqbxEfdc9Xi3J6+8DFzU+PeZF/6n87v9n7m9owqlLgqS9f6+cnJXWWd/fVplhD1Gtupa9tCZHRhr3UzjlEHCrWVrwwTe463Nh4K4RqMI3RzGK0Xufd8uerJ/VCf5KFSoT0JC8fAGa9xacxG5NeewfOHXHVOirrvc01KfqDn7/xaLzOJViCkqL8vWijr8503tXx9HPj3Ik2x+oDtqCfn6Mdtw8ahmEYZxwm/Kvh3LUpitNIZhsaRet8PfLoWgU0+hvyAAQVPZ35Ra6CeQGRES+OTjU8th634HcmtIlVBwWGJv2+o/deLun9QHVxX/Qivdrzy1786/POzSMy5nMdaonRaeAsn2vQqCUk7NRohZl6HW5LKYqQbtQK0waNRPyHCU24akf7K+1AaM3+W7iuZzsnct7iI01U8dkJHK1xzVFUGDe+qGrFz++768p+91zgt8nywlUPfNg5qFbCs1n0ez1GZwT/CLrrtBz37xuGYRhGPUz4V0PkkiLpPM6dICqX6XDuZNeuqYI654VYxieTdi4ROCQNqiimbiJs6bk5P7dBRJKjoy1PixWRn3P3cknf50WGKa0yEw4K4n8o0caglYmGEOn3doz6lX50zGOoEH1CsuxEE5Sri0eN+hdRW8iMF2ENJWM6R9En+JajXXzri9ZGPwepht6f6HNVK9rfV8UnnrqO23pwbhahpwmLzw7gRNXPdLOiX78rmnjcTHUdte7kgeXRwvRVM+mxH4g910ew9bSLLi7G/PyOtznWNvS1zpngNwzDWD/OHoW3XLk217rxjs6OZx7/KrgJHnMTPKE/uFM+SncKkW3+TyNJpC1evCIRONPRRGAV/HM+Abi516ER94JfAHQsN8E5cl/guQvbmRvj8NgAACAASURBVOSEbBuOv17JkpYs2boJvLog6InlDGiln9oXzqHJ0GNoFPpXGphuHr0Hneh94OhkF1+9b7UFsIrR+tF53dVITOidZPsAMO8XMo1F2UW2oGUqk6+t70Ezoj/u52/UZpTx79syGvgYeebSN24EznYH6zTqagbdgdoGjKB1+BtfZJeOIzH//qz371cujA3DMAyjASzi3wwqLnSbPkrOA/2F3JkqH5XX7E4egEaXp7yFZxwVXI0lGLZZ+rP6sBRFts9v58TQV7l86HKR0BTpvwCfRSPTDpF5L/7nKyLHzs2iPQ2KaFlQXSjUFkvTwFl9xaVXP23pP78M31dvokVEcmgjr4WqFqT1IUX9nYH+1fuhEfNqx2fKd1dEkH/hhzPbOZH3Fh8a6k0QRfKTPysq+hcb/h7p4kWa9PP3oYuiFSDMZ+YuvQen/HjtVtURYBv6PW3d0qOL163+p3Xx7/sCBz1uIjmx2zAMwzj9sIh/q6id5IT/5d4f2wnoTjUg5wo+Ol2kAx2BV6sW6c7CKdSvPk61CjvJc1oisv90ZBHpHM455r6Pr+U/wqsG/Hy+0ONW9vporXgRNA8MJd5vTdYd9v77UOmnVsdgB0wuHR74zFe/8+zHGnwtBSLB3NTOh0hi4m2nhF3zn78kURmarFWS+mH+tc+/94115w1VmaotjpoX/WEB3HAFIR/JX0HfqxFgBk0E7yF0ZG63wZ42lduKWplOAEiWl0mW65sYY9hH90d8dP/IeiXtZlzud3DuifW4tmEYhtEdLOLfCUJVGBWhW703f4VONOKpvFbbDcEky9nA45Jli5vwHngVcqEizhAqjOpbOPSYBUT6fPS/I02NnGNR5CN9D3BRnzsoOZy7F1ywahT8653z811IiPyfQmQrIlM0UulHqwMtok2PlqlnhdFqQ8to06V8zbFLX1e+rLNtKOnZqQVjhoRk3FXipTmrLVL18YqcAxFSh7kkWHzSNGYX6kfzIaaqPD+O7tzUF92t+Pl14ejQ93QUbWg14604fbTSGKzyGlvQIMpUybxEMql9+ZcWSY2CfKiBMfrQe7F+/n21T6UAhi6Zun06Pf7Yus3FMAzD6Dgm/DuJis9gBeqLWYHmO2WHKbtWzl+rxy82Cg0Komlxxb/43ck/yCFv6SkRXWrtCFVxUjRaAUgr64TSn7mGhFzdIVkW+f3iA1zU/yGN3i94+04k/rWCT7L4hylgHBVk894CVKvyi1b5CQuZ+l7zIirYl9EqOc1bInTB0UxJz9pVeDQSXmusnpjtqVpS7yhQ4kkXQT7Ia3ou4b5CzOJTr0FYBhiumtTanOhPodajRncF0uj/bzlUUA+s+uxDbkb7gn8MXYhMlXymooVcoXAwfX3VniD6Pu70x05VXRx1G723aT+PYng/TsFh9I9hGIZxhiBW+nkNUNtEL/qL9WTD0crmr7MqOJqIiKqgSrJOqDAZ8+M1vnuhUdYMHSr9KUL62Xx58Ms8x8W86RlUpBT9z0P+esWyk9Oo+Ayibxwtz1qtqkwPsB047sevveuhIlKrwGi1lbqvV4RMiUUmCP/q/vcev0DQXIKoK60rOy6NRuqri+N4vkNSKVL9PAxT1ttAhLRDBnFuVhqp4hNErXPJZTubE/3Bz9+otSfY1XJEUf5lohKdjScDJ48/6seZKZlTtANX+/sXfcYccKxr/x/UIto9cXS6f4hhGMYZzmVj4u5eo6o+cgf3OOcu69R4FvFfC6IymgJs8X/nZb+bBdxq7fD2rxNPBA5ivrZwVdGx4q0PvajYzfvnHFrJKOOF2kpVn3bpmCt+zI6U/nSOgsgVc9uZHPqKXDhyId+e95H/9Kp41Yj+ICJLJUJKLTnaw0Dfh2kSItqx43OILKCWnyXqV6zRhl5a0nOYBkt7Nky16H7yAqN2Yq8ujhZi4yYdO0pZ+U4RZIL9/cCCiFptJIvU+dzuolqt/maayTXj59dFSy+681Ie5e9HP9utR/k1obwPTeafiT3eqOAfRD97+XXxzkdiH6JqUoZhGMYmYlMm90qWF1Tdfu8mak056RP/FjIu983hwuxf+4hvJ69T8KLKeRtQ/VKRKp5X/Dm9xBNcnct7MVhAS4D2NziPBTQfYZA2y5E6hzvuts/t4aHCp7lmGJGMX9QIoayoXq+/4loqGpd9tFsTg2uVMVWxGBp01U521nu27I9v+OWINPXdi+rt16ZWhR59PhKmUrF4qFLm88/4lZ79HCoKzgFF9vN8apUMFdkJTCYuTpoT/aEpV6PHajRfk3eLaC+HqHxnq0JXrWTbUcF+YnWc6LuVR5Pkk++JJs3vRq1KR6pan9pEsohk+UfJUhoZ0u9zuD9aqrQDVjzDMAzj9GPTCX/Jsg/4f0/KPXbtuk7EuVyezHxeMgvACCLnoFWBOlYb34v5UNWmx/+pLR510VBtAbDsfcgpL2YaWVDkvCDva3jBUHM4Fq7l0ysf4GcH0YRajchXiv902YkrhOo+KiRX6iy4jgE7/Nj1vieR17+B1+g795aPuUJyX4BmFqipqiJZX0PoSlttgTBEWdlTEdL/jf/bJ7glIOUchfNWHnlEXPGPqlxnK5oXUrlTUq+Of3yuei+qi+noWPHHhnsabF0rq++Ffm6b31UTGUDzdIo4d3x1t6BS8Fezje30gn/JC/6ZxOM6gUjfvz7yQ+PAjwLP8gueMM8g9lc6Yb1ralpZ9kuW2r00DMMwjDVjM1p97gd+b0th6ovIefEo7fKa+1xFrlw8OOBWq9VoXfiz0RrxQWS3TyuJwCrcCt5O00c88qriWu0zGjmfrivQtLNu2tsdVtq5186xJPKB4kNc2P+/1Oqz5MVixu9OLPhdhuUSAarHDRK6+6qwS07iVSvRHGr5WUSj+tUmtOIjqiGhtHk7ifYGSBL59aL4jTJEPE+jMtqveQMlD5H6HM8d2MbJRSDjHDlE0o/Awzj3PytnKuNUK8upor9+pahQVaYRe5iK2pSf94i/9qJ/LamWbT26YBhCBfuJ2OP1q2jp93i7/2my7uttB51PWGiu/NDcnbOXLB8+9z3f+4VJ4vkv68SNO28Udt44AdwCvGo952IYhmEoltwb0F/2IUpcpENlKduYSx9akUZFdlkjpQ5cIyQC109gjY5Pk2S9CN1xG+1Oqq9P2r3HImSu45bBW3hVEPsp4lFvjeivVLw+XayseME+jAq8apHys9FKTavVTqocF5J8C8CS7HfOTVS3w5Qk+EaCMl0hVlUIO78wiJJ7y8W1jlG9oVXIcQiLi0rhP1b+/r1Fbuq5iRt7BBXhDikSOuVWjj/sr1+Z46C7ANMNiP6Mn1u9HQFB73Ue9fRn0BKdIXk315Lg1gVDaOw1W/Y4JCVUR8cMECXCTzZ97cbnmCKylenOVfT/Vq2KVWuD5pH0A24mNcLYJdOfQeQ9boL3reu8DMMwOogl954JxAWXRqbjuwFLa/oLNdTUh2k/j0FvOVhABVT7/txIHKcaElxRt97gmc6v3hP1Uqd8xDdfbhdJGCtE6Nsq/ak18V859xAXDt8p24a3OTeHiItF/hfR7sJQGvmfI+ruO+dtS9Uq/RxDE1WPEzUPSyIklK4Agzj3OcnK37kJfr+BFxJKeiYtFJIi/s3lpwS7R3S9ctHfT9mOhgjp4/x5v+DmWI32k9yrQOeeTlz4RdafepadXhqpzx8tQEOUf8EvhrSyUitRfr0/o+hn8bhkGejbt/SkpcMDx9B7X0vwj6G9HBbpZv39sLDS+Syg9yDUVV2/JF19P0aJPpMLYZdkFHDw9PWammEYhlGJCf8kVCRGEXa1hARPdqFlC0Frcwl19UOkbxciDl0YnGzbsxs1BGtlARCSL4Nt6JS3Eo2jUdPqEX09XqPXugBoqfSncxThspmdcnjoDnnW6LO022wh5sFe8uJfSl6XRojHEZlBK/yMAacSLpBHZBa1/EA1y49zDpElvPVCcG8fLUzfr5s2iRRFSHu/f6CaoHc+ml1LFAfLSxJ9xGv3Vy4k+uKiPVTxKbP46P0sRz8DAzh3MuG5bZQ3tao8JlSaqS6uo2P7/RzSaFOwUHFKu/I2a23Re7rFXzv035DMvpUPLKf6/yu6e1RN8O/w85hpeKerWXRBFf6PXkDfY6HWzs5aoIud+C5D5XtvGIZhbEhM+DdCXMCq0IjvBlTWju/OHIpoqchZL7b6gfO82FxItFg0P37oCBw+F4WqwifaMShfAOTQBUC/93XP1xQparfJAQOI5FsVNMfcznmRrw/8Ay8ZeQn/NO+j6D1+zCD+KRP/p3xEegq9r6MkJWDqIuEstLNxpsbCKI8uEOaKh9J/DyxXs+c7R9F38K1HWAz0obkGkDxotWZc+mQWcQcTknrDoivGm7kps59DrFbxiRKnk3okjOPcsbLHBdgGnKgp5iM/f+33XD+PoRnXsJ/vohf8zS/EdRG91Z97PDbnASCzpTD1fyYzO7+QsDOSRhtuOeB4XUtSK+h3KSR5L6LWMS0Zu16VePQ+D8YemenKazcMwzC6jgn/ZgkR74AmiwZxlse5ZV8qNO0mEiKknZnDCrAiWfZuKZx80sl7t33WC9h5tGpK63XzVezERX3thmCVCwC1awS7kpZCHPTzSrZL6TUXvFDX+vktLKacY1HkH4vv4o1DbxRZ9IuKHrSXQDzyHxdQ2t3XuSlEdL7J+RTH0K6+xwldgysnUCTqAaCNkZKaZDX/wpxftFSLPmsUOPm5YON5QPYVP+4OypvKjugh1ptBhJS3+MwDqZivv/QzpdfcUWFvaVz0h92leo3A+tCdjhShLr/uPvS1IPi1j4a+Nyf8fQ2CP41+7haOwZfRP+G8fnTrpggcaXuXrXJeGT8HUKtYeF3rE9nXhVG4T6C7dxbVNwzDOAMw4d8u8YZWKjKHevcu/XlRUs9Aei/ruEgo5e+m0lvPRkXDCTQaus3/4l6inviqR6moV6tJdfEeJaqGevChsZY+PurnVb1rrorAnLdWteTXdo5lkT8v3sdTh94dVfzJeOEcj/zn/AkOkWlCcqvmd/RXXFsbgZ0iEvXVFlcFVEQuEvmwGxH+jb1Pem/L34Okx6LnnFsiy4XA95eNNUzM0iaCfJprBrZxcglIx3z9Sa91B+UNuhoX/dEOUfVjQoWkPPq5XkAtXP20kheiC+PQOdv5na1Bog7TlY3ptENvWIh21r+v92rIz6mAfl/jAYS1rboQdRcH/by2byM0DMMwNhwm/DuJF64rWd4nrrgbrc8fcgOWupAb8DI08VRgtVb2JCoiB1ArUA5tmNR6DfHSjsChk2rybkYQZJULgBkvdsZ+6ay/GHv3tl/6npuo4knXpNyMF2bLNQVi4unkRH5x9lHOG/mA7Ert0uTP0OV3CZE+RHpi4r+IyPyq1UdkuOT5aOA5b/lZrGr5CZ2C1beeosxGU44I4tyq6FdbTe3XW80aU3Nx8YNzd17zb8MvPFz2cDr+Gn6e96ev4S6JWXwSG3qhzaymSoShvrdbqSX6Y35+2e92O0juXqsLA4f+/9TrF2R9tFKiU+1maVTIBivbMOG9SRb82/w8Z7sg+IeIOiwvE+VcrK3Y13scmthp5+l27YKGYRjGhsfKea4VmqinlUeUma78oldRExZ0s/7fQ4Q653rd9hYgKuDSNOI7jtda95YXyeIGigv/sHB46NVVbDXx81su/SmCPJsvD3+YV3Ohe2g25lUvrFpI4vPXx3q8wB9DxVCSr/0stEZ7ctRfBfMIKprn/fwTxfxqWc+ovGppSc+wAAkWpORSnpU7FPp4iOiH+vrxcUfRBWHRzyP1IBcO7+GhOdTi41ChnSsbc0vCWMFCM1VD9KfQ3YcVRET2FR9F5FlugsnYMaEcZwH9zM6jCxrtyNuMZUrfvwy6w5T31x9EP4sLFYs2fX6n/+lER/30pUm6i4QE3UaqGHVuDuE9CuRM6BuGYbSGlfM06qPCVcWr/hIeJerXlNz0qLXrRKU0VdylUPH0KLoLMIbILlRUnWxJeIQ8gEYSgaMdgF7/enPsdzcOFhe+BvR6sb1QdTESlf4cQsVfw4JMI+lXzO6WLwz+nVw5fiXM+LmkcW7ZzykS0vqYWkA0yjxOeaUfXTRMAcPeMlSt+Zd2Pw6NoKpbceLnDNBas67q5T3V1tJHvBlcyAmIvfdf47K+C/l23OJT2dhM7SArZaI/heZIVPeAR37+FX+8IPJUN0HcJhdsJmrx0cTrsOhrfKEaWVZm/IIpFYvwJwn+PiI/+9GOLcZLk3RX8Naw/r2LP7ic6v+Wm+Cxjlyn9hyGYnOARsqqGoZhGGc0JvzXAxUXUQnAKAEWVEB3JhIX7D3eYuMfXUATVYeAJyFSQCO/zZckrEwEhmodQ4O4Ful1B+WPUQHp/Ose8SJvPlHY63jz3qJTkXwrWcbdREIpTs8Rt3tB5IvFj/Cq0VdxyxzBxhI67paK/6i7L8yQ0NjKR96HgXQNa47W8w8WnNo9AEpGb+CYCBXSleeEnAx9HeXCeTT+mq6Wu9If4+G0sFqqtaKuv3+fpOTeNyb6ewi5IcH25lzB4UW/PqbH6P1aQJOi+1CbV6O5D8NopSu1rESCX1DBn9TEbQiNfHfGzqP3PCTp5onue9H/6V2R3ncBHwHe3JFrll4/1NQPLNR8bwzDMIxNx6YW/rKfn0T4dzfB4+s6kVIxlSZ0wlXmq/rpGx/fESLXKg7C+MfREolDiJyHir3ZRN9z/WuU5wEkJwLHFgB+QRJ2QoaAcTQnYabK4mEZkRVijb8ky0XA/ZJl1E1QdcHkHEsiHy3ez8Ujb+at84ReBJH470MrMn0f+93V7qC8BxVrC4gMU9mUbJJQ5Scpmq/RZuefC9+zTthHyqP7qcTraxOxWUR2E0/CVXFaiH4k9SA/O7SD4/MADtGymaX+fe1KXLprkAbGqgrLyM+fJ96HoHTcXv96QufiGUJH3kYrU+lnYQD9nhz3gj+I3/kEwb8V3RGYw7nS5ORW0Nc5TJTEHXz7RfQ+96LvETi37LL8F7SSVGfQXalQUz/fCaEvWS4FjroJjtU92DAMwzit2NTCH3AUcCKlYiqWbLn2qFCJ7wYMx+w09bvi1h8/T7QI6EMFN6g4FDT6vh2NvJ5qetGh8y94AVY9ETi+ANDrzvtrDgDbEVlO3IUoK/2598KDD9/bd8l/+9ZDly4hh2omxjrHisjvFx/j3JG/4oZFYBn10Ieyn73sd5eKK/4k8HZCZ1hdJAyU5BloougUatmarmLVWiKyWlQT/U6ElDYi0ztSbf5VCF1sK8YllJktjZoPx+/rHbywv0hKLT5I3h8f3cOQrxDq3etjaXTXoJroL/Hzo7kC8TGFKN9Fd3pUHPc2bOtRW9QQoVOsft5G/LOruQux6+1E7+1UR6LgaqMJ71VYpIQdsD6ifI2SBUzbYlpf90Dskelan/kW+STwj8DPd3hcwzAMY52x5N4yREiVPxYTZeuLCukhImvHXMd+6WvkNPis51HxMr76czza29y4USJwtcolUXQ4+MAzqCAMwq669UmFUNHvBmiCbJ08ABFSL+X2kfdzQ34HxxeIykZmUJG6HHYAfER1BhVbuQqBH+VLLCTuUmiEOXi8E3dBROjx3XHV45+c3DuIVicqTe5NSuyNFlOjaKJqMfZ4JuzoXC13pT/Oywa3c2LJX7/U1x8EczwyHon+5M+DPi9ECbWUifCQ7B2i/ItEnXvrf5Z1sTqMVsma99cLEfdywd+LVhpywLG2/ftqgerx4+WIKikFsR/GX+lgrkB5Tf2llnbkmrmkln49Ucs+ZxiGsZmx5N4ziHKRL4L4xYArO27tV0yhK25AZIRQqUbFRuuCQM8NvutQGUgbgsGgtwIFG07tSjyl40aJwOqJh/JEYP23RodDkqdW1llBO/ruQBcfla+vvPSn7jb0UKPpmL7HPz59hZw18jZ+e/R57t9DUygV/youV7wQjnf3HUU7AcfHnQTO8fcmyZ4SFlHBa9/p6GzFQhWNnM/5vIL4XAdCtF8EuZ+fH3wZH4976st3LXZCLEIdRf+rif4M+j0pVFh7oig//u95dEFYWsWoGvq5GEF9/ye8pSw01SqtkKXR+BE64d+PknSDbSu8x7nY4700k49Q/5qjRP83O9a4pr6b4KG1upZhGIaxtpjwr4MX+CW/dBMWA7IuuwLxSLj61Mdjzyb75Bsbd86PGaLGgtqPFlHxuw0VsVP1ouuxMesnAicvAEIN9yEfEZ+tiLq7kqRUiS0G0rXm92V3xey58uH+P5OXbXmZvr7QCCuIuRV/7Xh339JKP2r5OY7mJ5xMuN4KGpFupGpTDt1ZaNTuUuLXL3uupDkXZQm7X+DKvh5yK5/latG2wGW7MbrYOh4T7hnUJlRN9GvXZqXc2pNB721I4p33P9cv0RnlpOTKBL9a4krnvAUV4wttCX69r1H35ej9KBDtVHRO7Ee7GGGs2Ya/V4ZhGIbRBKet8Jf97n+nKXw4fzBz91pfu3wxIJJoEXJruiugYjj45oNXP3iQV2ihDj7x6kNqORhFxd1JVMDt8JHyOZqJSpYmAmuUOC4UKxcARZw76YX/GCJ5dGFTKBt3yY85hIrcfL3o/2Pu3CWRjxffwxvG38BfzhCVPwUVkcuoyAvlPSsr/ehCYxTo9zsCpbsZIrOooO9DOxJXa36WR6RQYbkJVFbwCUmk8WMG0AXadkqTV/ti0f7Uce7r2c6JJYcUKWvk5Xc4Iu+4vkdDOFdp/Qh+fl20hJyCuOgPfvcQ5Q92rNqLGxXfW9AFSRD8o/71RoJfP+M70PftVJuWtOHYI6GTbp7I3hP6CbRrGQo19aMcAedOtDWmYRiGYTTAaSv8Ebl3sLBwAhlLlz2jonwtt8ar7wpUJGquyWJAX3vUqVek30dDQec53fT9UeEckoJDrsECKowzwLloadCZmp780jGDsExOBI4WAKnViLJzk17Y70BkGRV7rmzMeS84e70vvmb0X5N+3zt7nO1jv8cfhLnHEzWXUdE3h4rDpEo/x4BzidVsj5FHbS2O5OTdwmojr+rHCCFhNiITF9CS5Vd3PPXo0WP37bqN+E6A2qAW9Z/I/Vw09IP82yIqokvH1IpSUV8JfV8GSUq0VnGunYQrrT1aj18XTVodqWy+iago3oYu1o4j0jO299RNZ+859JnDD+y9K3Zcxh8nqH+/td0tvTf6OqIuy/FKTJ0S+8NEVidHrWZnhmEYhtElTlvh7yZ4nwagE353qlCsiMC3LA5aIEngi5AqWww0vSsgWcRNNLl4ULG15CdR3jxsKTG6XHu8KNdALSRpNBdgGfXkP9n/e6qhsfV9KfoIf7BSRJFzfT4sAIIX/ShqxzgLkTlCz4JozKgZl0aja3r/nSMn8taph7hw/A/43YUn8XiOSPwHm0yGqFKPJuOGnRSN7E8CWxE5XrLIUDvQEiqCexApmYNzFEWIL2AbfX9LPuNpl+/15Ti3QUn1mJ6QH3EL1/VNsiP3dQ5EpTsDKk6Lq6+ptugPUfB8gujv83PrQxdLGcq7JFeOJ0SJuCfQXIsxoDCTHv9vM+nxo8Bd/v3UnadWy3FGSbqCfpbCex3eg2BDal2Y6/0JVYa0alW7FbkMwzAMo01OW+Ffk+TqKuIFSlJ32TVZEFRJHA4KXFYPq7UYcO5Q376V31s+1PexFidR3jxskCg3wCWKvNrjxRcVQ2i0dAYVVFt9pH4RmKwrpPT50Om3Mg9A/172i7peogXAEFqvfpZ44nFU+rMXtdosEDXdqhChKsB/ZmqasdHf4u2ZK/lSiACvEEX+00QRfPW2R82/Fr1YHaiw/Oi5YZGkQrnD5A/1/CW6ANsVE+GrXn+t2X9P7x4eWnSUJVmrRSizaudR4TpQRfQn+/n1+9Xj/xTQ+51qIMq/Bb0nJ/y5I35ueu0se9xBGUXYje5GNO/f19fT738KSbphd0VLjrYj9nXhMk70Pe5ITf0zBcny+8DNboLvrPdcDMMwNjNnpvBPQn+hVyZC6oIgqTJK1+1CZQLf6XQqFwMlCwaR3027wheIKqiE4wotzbe0mk+a0gTh5E661ceKJ5OOoIJ/CRXO53ohP93Q4iLKA8j49ydpARDsJOE1jPrFx0yJ2Az5DxotLqDR+sTov74nPz59jYwP/TZvG3kRdwTff1z8Q5QL0Ocj+OGzdRS1/CwTt/xo59ryMVpHKqL1gn4etlDaICqNc3kR5GtcNvzf+ePFW3lFgbivP4rsn/A/9wL9FbsoUdnV4OePdtE0yh8SeEPFnvIKSOWvITSfOuX/Dkm8cZvaDqfPNfa5KR0/JOni57tEqdjXiH/rYj80D4PIQtfpqk2nH/o5CSVlARz73e8Bh8GEv2EYxnpidfyrkbwYgDXOH/CLgOgXaPhHfNEQmiSVHldse54qojNEC4vGvPulYwhRp+Bgp9BSi2oFaizpONSHL08Ejp4LUfRU7HqnKhYuKnLDLkSoM5+YbHu+PNz3J7xp+JXceoq40FcBmSZYflT8xRNO+1HbyjFKk2Yz/rXnUYtVLnpKu9w6pJ94Lf+ojv8Q8T4CKs6jBYf+HGrsH/WPjeJr239GrukbYTZ1OXfnHJKKjZNCE4GPxcZJEv3BB5+PvQ/Onx+i6aG2fZHkhmZhrFF/zrSf8wDxcrR6re3+6ONNienSJN0UuiAs31VrTeyLpPdfOPGjo8WZ6S9958r/RHcfulpTf62QLOcAP+YmeE/jJ5U0YoPS/39K7rFkGQNmmrYpGoZhbECsjv+ZSLVIpfrMK5Mvu2QXaiBxWFSDUSmGS6fZfI5DaQQ/4y0ZYT6NNQ/TX/7BPhJE/7QfYwyRnaiQri3wShOBM/6xeKfZUDseNOKdQa1GeXSBEXYLcmjEP9g+Vgg1/MvE4MPu/GWRW4rH2b7lF3lPiEovEXn+NadBI9yjBAuVVhfKownQ8eo/ef+elCek+xwDn0iq61QPOQAAIABJREFUUep6JT3LE337/bnxXA7BuaII8iCP9nmLT4rSTr87YguFPrT6T7noD/9PFMKY/nHNWYh2XWrbZXQXqJcoH2TYHx8+H1q1SRc0zfn3oyTdNGEHyz/j/+Ra+o6qdSvsrhUP9e9/A/AAzn2m6bE2EtHujQMY3Dt35UJq6N2QIPwjax2UivzSZnM1cBM0t1tjGIZhdAUT/s1SPX8geYegCwuCcotQcgUhVyzbFUjFBByU19Cvf1EV0NF48eZhORpp6qUCPYi8XlQoBtvL2f4eTpNUNjIaI54IHAlS51zZAqBI5Bnfjcg8pSU4l/xxUeJuQqlNFeTvnnqEJ4+/jTfNEiX4hvMWUaG+iMjI6q6Ic8cQOQ+R5TJxNIt6wStLf4Z7FN3XxojKfI6iDcXw/54B+BJXDP8i7166mwOlFa900TXp/x2qIJWLft1ZiOZXjAnB+GLDUS2RW3cr+tFE37BDEhf8o4TFTjP+/TDnaEGWo3Tnq3mxrwvCwdgYMyW7Rll+mC7kZ3QVXdhW7BrGF47zcNvLz/3YAPKK/tiZbvXvBgW+YRiGsbEx4d8JVEhVRjlVbCeJuPZtOCWXT6wgFJqMoXNzzpeLrD63ZiwVpc3DemK5Adrsq57gKu07MBB7JoNWBVLhXs1KUashWLQACA25jqIJt+f4uc3FXu8CURWa5aTov3MURH775BRbxt/Ae5eexTegVPzPozsZC4gMxuZ8BNiJyJHY7kQekTB2qU8/oogmIIddjuDfJ/Zz/P6GvIFBb79ZrbJzs7ymb4QnFZfoLxzg6+mYxWcbYSckEv2zZdcIfv6QY+H8/elDxXaoaFRN8AcPfMjB0OZkYQEmsp1QEapRwR8l6YadiyWi6kb6WDNiP6qpH1imRlKum+h452WdRpZeoNDW+JHlLRA+M6W5LPq9SMd2vQDcx/Q4axxmGIZxBmPCv5vUtgsl7RB0bEGQ0GSsbFdAFyuri4bSCHo4r7H5xMt76lijY3unf2ukOHv4u/efe3tdH78+H0pIDqGCXhuGaQfZHOV++dLzkxuCxROEdYwjaGOz3egCIBKu+n4En3oGkSKxhZDep5+feqWMjr6WD2ZeyL+Fjq4DqOieRcX/MqEBl9a3X0ItLfFdjBm05GaeZOGfI6qMA1EFo/CZKe/Y2+OvEaL9wzg3LULqszzYfxWfW3RIZlWg6yJt3ucO9KO2objoDwJS3wfdhRB/7ABRr4J84mJRjxtG39NFQl6Ec6f8Z3+3P/JEQ0JT35shoqj1UuzfrYj9YUqtK+teU19c8UEnqQngh+sfLNrToZLS3ApdJKWAlF8YxysOtZ9gbhiGYWwIRH+vfwDIor/XXuuc+1LSsSb814PqC4JyX344vu0oY41dAX9BB1AoswdlYtFpaHwhMDOT5XcXUoN3AXd6K8cyKjxO1RyjNK9glKhu/i4v4Odx7niVc8vzAOILgNDFd5EQoVf/9pQX6dpkKvKtLyVF/29xr5x5oYwPzjE8/DL+bo7SyP8cKlALaKWfPNqE6jxEFlfFlgpu7QgrkostaIoipP3F4vco7RcnQaz2EHn5Q9Kx+Oi9zhn4ElcMvZc3LPnSnaFMqiZWq9UpiP6ovny0+IvyJ0qj7Nr4Kyl5V3cOhv290IZdGkE/hUifF/xF4GhDnyMV6MG6E8Yr0qxnX+9bvCvv+tXUV9E+QHlC7L7CR0iqeBP1tSi16sQXTJFtLm7n067DJvANwzA2A+8C7nDOvVz0d8JgtQNN+G8kEgR+/76lJ/fvPTV06t4t91FuJ2ozf6B8MRBbCITFQKHkOF2YxG0ntRKGz8pLzxzOzcXsIaBe/gU/xlJN73Dwm5dWBur3ViDQiHGlgIvyAEoTgYNY0vnM+z9jfrF10h+zgkjwoedJiP7f6V64cJFckJ9iy+jP8dczlIr/Bf/vYURCpZ8jaJ7B47Fx5v1rWt0NUUsRQejFkbigR0V+eA9CJDwsmAZxbvpd8iu95/Ck4mXcHSo8Fb31RnBuntB4rVT09xBZiOLNuEITtGXUm1/6nut5Y/75JSLBv4DmgmzxP9e381Qm6Qa7VsqPUf8zH9l34lV81r6mvu6c9MUeCe/ZYvnCpyjyPwFBKPfjRwvPyJIWxg5j5BMXYoZhGMYZj2gQ83nAzwC4uJU6gU0j/CXLlcCT3QQfWe+5NMNyqv+Ly/SfjQqr2ZgI6Hj/gdhCoIpFyBUBWe0rUJonUFJq000QibxQSUeZ83YeQRtvjROJ35nEuZdWBgolMR2wzXvVC6gVaKXsvGqJwPEFwLSfyw4v+E8QNf4KzahWfFQ1F+b3gLtoReQD0yfYNv4m3haq1ARLzoo/bwzd4VhBJFT+mfJzW/QidxiR2rsg4f5GoteVPK5i/4gXhisiyD38e//1/N/Fg2RTPq8gVOyZQnMqUqu7K5GfP0T5i/5+hSTXBZI67+oxY4TypCHJVhcW2/z9q+/fj5J0w45M3t9D8eM1IvZDAnG4P6faXRg3hb7W8v9Pi5TnqOh3NuSkxCn4+x5q4IOK/LALqM3t1vI1GYZhGKcDT0GtvjeLyNOBe4BfcVWKrmwa4Q+8HueuQVJ/l/BcpajZOLwV2IXaSMZigiFuFZghKrGYXG60xQVBwq4APmnYhTyB1ePiFptogEqbUvRhnPPH9/nXcY73xOeBhcQoZry6kEioqJNGdxIcKsBLrSSlicCRkCpdAJzw45yDVgCaIir9GawZPcSadTlHXuRPTs4ysvWX+Iu53RyNN+gKkdlRnJvBuROIPNmPHV7Xkp9/eZJvKOlZadOIrD2Vthytvz/9OXnu8If4qRUv+pdXF0tqOyoX/SmiqHoQ/cGyo17+cruInrOFqANuqKpTQC1ZoDsotWr5Z9BdlbS/ThD7IcpdW+BGi47AAqEBWbfR+1OetJ8jnssSFlOVVp2it3qV18CPf2+X1zvnoFtIlquB33UTvGC952IYhnGasF1E7o79/H7n3PtjP2eAZwG/7Jz7ioi8C/jvwO8lDbaZhP/PINKb6HlVMdiXcE6c5ETGLuMm+Ev/LyhNEFVULIzEov9xwbAQE7flCwL9d5OvqXZfgdXuyKKHSrAkRNcr7zAc/PfKnBem2sVVzw2lIivrgOt7GRJW51Eh1Q88BREtHVp+XpQHEE8Eji8AjqHdeJ+E+v/nfXQ+lP7UBY4/R3c/fvf4G2V060/yt8vP4SsrRJF/FYgiQ15oPw48CZFHCTkFunsRGoFB2DWpXtIzbvkZ9H+O+vu29D55XWaYJ8tL+XuNEOt7vg3njpZYfcI9CF5wfHO1yFc/h//MS5YLgOPuoMyijckcUbJxqKqz3T9e3b8fJemG8qNB7EMj3y/dzgz3pbgmQr+04lRgpeT/Ef0cBTsWRJ/10OE4nkicXn1vz8QSmbozlhR4AGD0klNbZ1KjT61xiGEYhlHK8ToNvL4LfNc59xX/88dQ4Z/IphH+boIi1ZoihfKPtdCSlbXul7AekTq93kzicyJD3gIBpTsEy17MSkxgtpw/kJA47ERIiS4EiiXHVSYwF8sWAiFqOufnphFqLcWplWRCRZrSScQrAw2jn+0t3krkUCvQUuz4pETgsAAAXQAEK9IJf27wxgtR+dACwJ+7N568XraMnGJ88EXcuUBpKcwMIv1oQm2o438SLY+5CPQSle8siJBJ+BCFmxYSfUMuRuig2yu4mX/hRWN38kMLr+ZvnY/e7wKOrQrYSPSH5k3huD5CVaL4bouIsK94+3jx1L8Bf0zUuTeU59yOCuHqdp7SDtDLq9etJ/ajmvqBGapVdmoXvZ/9VCrSyI8fVTuKV+UKC6d4pZ0wRi9sgBr4T8hu4Gc5y/1hy2NU7lBUo6YdaRr+Dv1jGIZhdADn3BEReUxEnuqcuw+4FjhU7fgNKfwlyzOBg26ienLCmlPPChS8uYkum2iUNU3Cq9ZUS6SfqO5+fEGQR5Nx4wuCOA2XG13NA1i9JL6vQKkocFTsRESNxVQUhuTTGUIVGvX2Z4ii0qUJvtHPp2JRYm2mpQu878WuUZkHEIleUIE7XLIAiEp/FuLR/w+562dfImMD04yNvIpb5/xcQ+S/39uETiByASJz/jqzwFlocm7BOVztj9Dq+9WLCu+TXlgvfJprBj/L85bexa8KmlewAzhOqCKjybbBz1/wYwlaPSnt72eUvKv3Y/wXTr7nvx5Yuue4P0ctZ7AD7d6cLPh1odFDZOVZ8ecXqor9yEIUWKIbSbn6ulSUxwmLzugeQakfv+j/xO1sIaJfSNxNXAN8Dk7YRangxMEtsmV8KlX1Y6X3o7yUb8kRbIQFjGEYhlGNXwY+7Cv6fBt4TbUDZSNaSSWL6y8u/sTi4cF/Sni60Kj43HCosEmq2hJnXSxFQBAAw1Qmj0KUBJukH1rqP1DZbVh9NBXXSK4Vr8nBKixD7f8cGhVOOj6FCtYetIb+App4erLsOCGykxRCNN0/NuLnNumj5CEhdYVYk6T9crDnV/izsRv4wCyh+ZWKb21upo+dB3zHj7/Vv86T/r5kHNJLZMEJicUrsZ2DUWDAW3jG3scN8ykKg5cysXKF+/ISWklnniBSdYcn+Pnj44ZSq4uxHY+Mfzw0Cgu7Sjv8Y1NVLHO96CIn7Hbk/fnVP9P6OsJ3oki9cq/NUpp0G7ebLceer+xqq5Tvhgnr0OSqtNJWMuUL7djJcQtjtdcZ9bwwDMMw6nLZmLi7r1yba8kd3FPH6tMUGzLiD+xbSg08kPjLKOrS2ihRBHm90XnUjgqup6VI73dSHoE206qchyYWa9S48k2pc98Ty4nqQ6vn3cor5BWl9yPU5ndEuwHTXnT2op1yM35e+dVdD51LSAqeQhc4w4jsRC1gR3Eu2DrKOwKHMp8h2nsWIis4N0lU+jODSB7ncgfd/pzIX00dZffWG3jf7C4mgxd+EBhF69pPowuQ4+hi4GxEZmOi0hEirdEN0o68kS1lEa3LP3cJh4fv56krV/CVHFHfBC3PGeUopIjyA8JOyAwa5Q8df0P0P9yHRTQKv4Ow4Cl90zJ+Lhr1VsG/QvXmXqGmfvi8zBLKtraL7giV57rk/evPlDxX6sePV8eKn7cmAj/WYbsqFcK+PIggFf8Kr2PltA2UGIZhGB1nQwp/N8Hh6k82GZmKkjihesSr9LkuRb8kS9pN1Mkl2IiWIhV70wCS5VnAN9zEap33EUqFU5jYohfF1cZLeDgpcfi2eNSZt3AjN1bakEJDqRXCYkDtLz0+NyCPCmGtfqT3eAqYIqpTf64X9svA92S/c+x3/W7C5xroNSNRqwnA56D5BqcIpT/1/ck75woibz0+y8jWn+f9SxfxUEjmHUJkLGb5mUarvISFwTTAE+zmLCpcNH2oENeFhtqExu7kB/sOsTf/a7wrT0hAjqLTSzGRW/DnhZr7c2ipz5DUG44L0foRtMHX0ZJZ6GsMeRTE7nuljSeqqR/ItW3fqdYEK8ofSFMihSVYjqIKOkrIM+ia/a6RaD2aqRNf4IX5Qphz6Qj6nbDmXIZhGEaTbEirz7qioqKe3zU8F/6drxdVkyxno1Vdnukm+GYnplr9Yg1Zipre3pcsO4GjZ+W+t/9795/zgH84+bVrBZnyKiegIjHJK1y33Khk6cHxoxzi9vi4d3MgdYCvR+OUW4+iBmJj/pxFNIE1Kr8YzXkQ2PWcp3zx+x/svfDiyft2/UYsuVO7oyoFNMo9hIrQU2j9+mDpWQn39xfl3eOv5KPF5/H5sBAa8vdgAbgA5x70czwL+C7OFa+TW3tu5ZUDfq6hkk/aR69H/TWX/oafnisgI9fymcXzeSQkYBbQ9yWI/pBkPeLnPOMThFOogA+NoZb83Pv9/SmNxOtiKhwb6u1XRvajmvrhPWi9pr7OMZR7DTg/1/Da4t/JYJFaHcH/Xdl4rAM0EK2PRH1pjf743Mo/9+tn9zMMwzDqYlaf0xjJ8lLg626CR4FQJae5Lf7IEpK0KHCAnHfRw4OP9j6Zyxe+uhN59lZiUez4SLFzcpQ3/2mUxixFGWqXMK2wFLkJjkmW5z/Rc/Zhbw3RRVLy7kOOpOZLavUYiV0jUESksgtv6YLgeQgfv+IpX7roS9+58nE/vhO5p+Q+foarU1dHcwqR7zmi3QAV+OqtD7XoT/n7vQAc/63Rn3jiP/qffi7wNERmgJOEqjJRHkCwD82hFYS2otWACuiOQAZYebdzp35F8iNzjAz8MP+6hIrmQX/cSUR2eNtQiNgv3MZ1Dl4ZVfIJ5TNVCO8AvgMMDzM72s/y0vk8EoR9VBNfVvMEMmgZzkX/OpzfqYk36Or3f6ZxLrJ76UKm389hGZ+EXLJo1MVF3Ao2TyulNvV+lS9Yw/cx3Ivwd8jrKM9HWeiUtaWpaL3uCCX9fxoyWcx6YxiGYaw7mz7iL1l2A8fdBM1Fv/2v8/gWvQiSUNoyfq0RN8GsF2/BepC0WAAVEfFShrXmJwTbRiep39ugtqUoSpxMuifFkqimCqfh8FPZsbOA+6Hz/jXzyeEXvDR/qOdjfsx4jfVwjSUpcwvdyI3yI/yTHODrlY3FopKhYTdgCb2XS/558c9tQ9+PGeAJ7/kvTQSOEodTwFH0PcygC5T86+R9A8/nU8Ov5LYFf55W5YFzgUdXz3fuuz7Bd4hgD1I7UaiUMwgsfpTrcjOM9NzAX8+jwn6ByKaTJrLr9KG5Gzl/brBnLRFVtzkeuye9/riQuKxN1Urfr/HYa8+T1GehFlHCdOnir7Ssbti5ChWIWP27AzaXhpNmda5JfTJWh2Idkn4NwzCM9cEi/qcxbqLSSN3QeQkCv5bo99ea9QcWgWJ88ZCwaMgR1aVPsh+VLxjiTbygVCyVWwoKaFS29qqvnrjSGvj1FgfJliL1lZdHdxeI/MshwVZfG6TuePRFAP9GVF1ngcoFUb+LtiBWo6xCJFpfwa3yArkhcwN/Fe7dEqEEqIrrEUS2o0J03kfAT/n7Ow7s8ZeYAR6PJcYW0Yi/JgDrfT4CDCDS8z5Yvoyv5eYY3vaz3DyHCuptwCPAU7zlx+8UOKj0qedQgT8zz2AxTX7guXxxhqhKUc5fM5R23IVG6I+j4n8rkL7q/LuyD/ZeeODx+899P6Ecp74fY0RWmUXii0mRAaJmVoLukDRmR0lOuo2SqEOvgyjRNrzuIuWlWhskJurLF9Sr3MoreAUfi38GK603+kjN2vSGYRiGcbqw6YX/epJU1SYsAsLzviMuzpGrfLxknClKBwvR1KQdBbV+RPak4AEvFzfh/LnEaGZnLEVQLqx0QREvs7hYdnzei+1hSndFwmtcKZtXj6O0s/HdHMiJ7ynwFm7kR+VAZpYRd7WeexK9F1oyVJN4g+VkGucO+fltAS5F+wMcQ6PqggraJ9Bo9bmoKJ8Bhu/m8gXBTc4ztONnuHlxlPlltAmWWn5gEhXoxx/kgtQevo0fM+Ov3wek7uKq9ADLC/s4vMXfnxWi9y948SfRqPpuf34RyH1t4PKXL6YGfw74X17sB/E7jy508oSa+iL/P3tvHi7ZddVnv6umO/eobo2W21bb2N0Xy1gK9oNj3MImwRArxqHbwQw2GYgcE8jHkHwhQF/5g4Qw5CNAMFPAOIZAi8RgB8wQoxYmhEHyWC1jS9Zkja3ulrrvXLeqdv5Ye9cZ6lTVqeHeurd7vc+jR92nztln16nqe3977d9aq7WT0jMpNzvpVnc8dEFSJFQa0mcURH6DTj0nOt6qu7A/xfHicX67p//eEmQNwzCMKwkT/tuE+CKg3z+n6+E7hwuiNnshQd05VhO7DJH9KItpQnfbpNc6RGzjC5gmulAIlow63W1KavvIKgeafFNRJ1nNK4gL/PjiJiTdzqVGCPNdAbiV+8q+Vn5r/mc4Uhc0wn03xwoAx7gndARuoqJ1j7d+gHr+P+0j2tcQifzHiZpWPYGK4WvQij0TDmkCZ/8//u3+f8B/bx7hb5pEfQIuAAcc8uzH+TJHsmLSbmD1MxwtFmnyNXxk2r+neLL0QdQadcH/OUTS1/x8Vz818ZLv/ujMG34YzRVYJBL7u9DPGkL+QaddIV1Yxhd0YaG56u8ZRH34fPT552wCFUuaTQj745ziu/mPlYd52h3i0W5DjK2h1rYh499U4Ujju50UFl2VXxzHlPrlTpFDwKMnr3RPqmEYxogw4X8Z0Mt21OnPENtlQOvXp3cTPG111k+ywAJ3Zn1/CuhuQlbFkqhbbPTa2pCWojBWw4vXEFHOiiAXSC4I4vO4dJT7Sw6ZDnN+kmvdI7yw+SIeqfmFQB04f4x7wjVzfjcAVPA/gS46DhGq56iNZxXdAZhFo/sXgfIP8iPPv4cfmH0Tv1f+W3yiiIr1Q/66XU9w/car+ES8S+yeNSbOPchNk1/Phzb8+1n072Pa3/NZ1OJztX8OK6jVp+TnO/cSWHlJ7cEnULE+658x6MKgvaZ+exOsMKdG4vnr/QpoAmtHG1DKW9/6PvwC3154BZ8uAfQQ9pvTx2Lc9Fr8DkLqObl5PkS/xQuGRTKa8nWuaJTmYeAw8IVRT8swDONK5IpP7jUGI3OXIcOKFP/z3RwrHORs4UiyTUNo/tQaOgxJJC7jLGb6rXVHotOORSDYd9KLj1DWMn2vkEcQRG/zSa6lQdH9Ba+pHeBZeZYDheP8tvPn7Imde8mPe4Mf52nUjqUWGn3fl4Daf+YOjnJm9zE+Fnz8NWDPCX7r0VO8bS427/2ned3SK/jM+j6eDwm7RXQn4JK//xwaZV9EFxihIZfW7ddzA+ttHnpdYIXnGMplFojEYrCFQYcSmVnC/iN8Tell/I08zTUC8Br+slPC985KkB1crGcL3634gZwU4un593P/1rXFI/V3l1z9U+ufnfxY5pkD5kjcKXL1yXQfCcMwjDGzk5N7TfgbW0rGbkJHTnFcjnKmJeZnWZIb+WK862tiaP//uHCsJWr1q0Wn0/1DhZ64wIWoAk4oKRn+awIbT3JtE6DEhqwws/4IhwA4xj27UK99WLwUUe++Q3cB1tGofBm48FFu23Bw1Ru5O5SFXP8fvOXZt/I7oVnX9BNcW3uc63k194LuDoS5FYl2Ei6RzAkITbvCe3ou1pcg9BwI/Qkm/LzCAqhOyJnwwi3lreckC9zOhyYBDnLWPc4NXNMerRc0j2P8tekHE+rdo9PD/hBVm12neeUZO+vaXtcN/4M/9r5lnjcBD7kqnxt6XMMwjG2OCX9jS5B5tYq4Ko+Mey7jIM+i4QxHWvaja3iqMsH6pIBMa35wdpQ5imAv0dn+Frzy2hxLRX2IiAdBOwXUnubqFXCuQq20xtQquOJeLlw1RQ1UpO8l6uPwAHAdUHyGA2fv5Zb9b+QPJyZwjXVK9cc4tPwSHizUKF71CV619mr+uoYK/FCmcy9wHhX5YXESr5izhO4gTJEU+KC+/3CuQ6sfEca4l1uK+zlfDFH6G3gc/f8T6ee4ebXpo+j06MbvNle9X7ek4E7XpiPoDmDi5Wt/f0PKX9O8v3jHAGP2xn6AG4ZhbDkm/DcRmUdcdYS/9HcwMs9vAm9zZySduNqLGhpxvSKfY7cFQ7AfTbDO1Tw9K7iW6FtlWqZZIbVoCP0XWonCJOvRV4g88KAi2y0xU1+nsgIwyepEkcbMJBtF1NN/Poy1QZG/5pXyCj5z9Sy19d0ve/6fHV0/87lTD7/1o/t57tIUtTU0KXfKX/MsalMKdfqb6OcdfPlhXmtnuar5IC9pvpY/X3+YQy17VRD21/C0K1GXG3givN++uzsDm+FVD888i/TxXvcuojarrMVEVEZ0REy8fO3XaoWJr3fV/DtdhmEYxvZmJwv/7Z/c69wfl442vr9+f/mvxz2VDqiA2BpR/UvAA33VNlcbQQktS7lZ88oqqTjosXTvgXCszoD11Lv3VzjdIIrYJ0qiztC+aDjJAic4VTrAs6UZluf85BLjF2gWBVeY1Ai/ADLL8sYsy6FJWHmNieYqEw1wDxVoTgiNacEdLNNofgX3rX2Ra5o/ftU/ecNiYe5b/mrqy+t/MfOaT33D8u+G5luLqOAP+QfPAY0VJosrTBWaFItl6usX2b0GUKIuAHVK7iv4P/hqRq1neaibUG9/LStRM5D+7ErowqfXFy/+/LLOHWUN/dDDYktsR7V5vgWtrmQYhmEYY2cnRPxfBZxx1R714sfJZlTjMNKU0Gh6WzWYGHkXEf0cC82wssRpx2PPclXrhWlWiiXqcw5ck0KtRN01KVTAFQRXLOBmwVU2KJabyK4ytWvWZWrPi1/66HvOlw7sK7qNxpetffK+//PQl/94g9LjG5SfLVFfdUh9g/JKiXqjSLNcoDlRphH6DQxDx6ZXGc+iG3V6VPgxDMMwjJ2GRfw3EVfl4+OeQ0+2++ppJ6OLqiKRAI9bNLKe+6iPZUWbe157gHPxvzaIKvCU0fcSLDQl1LM/XaZZAmbrsPj5ykuuea64bw9AQ8rF+yeO3PQ8ux+uU1lzUBEorzOxvs7k9AYV9zy76+e4an2NqVBhqMUK081f4p+u/h++ImvetV4dpw3DMAzDuDzY9sJ/OyDzfAz4MVflw51PaiUFFomEqjEagh1nIKvPyMi3sxP/7MN3Yprkv7XdaJnNuG2msoHUHO4ZgY1D64/ed2Pt0VOPVV74VsE1b179xA/VKV0qUi/u4RJlGk/Gro/blQIhebgGFL6N902RiuI/zCH5K7589rd6vKsNyq2FQZkNSb92ihOrH+b2TgukbqPXnWubt2EYhmEYm8S2t/psB2QeN9Nc+oGlz879VI9Tm0QirGk7AduMwSxZ8Y7G8d2GIO7j1X0gqhI0gSbdTqMNvDb8eaDefAc0G1BfZ0IEJwUaexyF2c/xsrNX83T9r2ZffdVbb/zgx6aaqw888zcH7wB5Yp3yxf/CP26+mQ9f8yIeKVfUv/+MHzNtg2rSbo8KNGLHBk/81hySUDGoF4l7fISvKV1iV6IMTXHOAAAgAElEQVT3whSrbpWp1uf0e3zd6n/lW+Nz7Va6slNN+k55A3Xb7TAMwzD6ZSdbfUz450DmmQFWrLrQGEmWdczy+ffz5247XXEhOomK9dBxmNgcmn6cUMFnJvY6qNBfQhNxK2iUvw6wyEz9Ga5Z2s1FmePiNZNsFIDpx7hh8af4fy78G/7dxAHOl86zr/Qd1/3sl76y9sln/vW5H7tUozy9xuQTu1hceZoD7sPc3vwob2z+HHccmGF5dkKHP49ahyBZv1//H6r0aMMzUMEer/4TnlE9dqwxlsZa+plP0d6Yrde/w54LvIc5JO/lXfUf51+ldxy6JRr3WmD085pZrAzDMHYoJvwNoxvJTqHd6CbYs3MpdOwKvcVgvCRkHRWU5dS8gqAPrPi/T6MR/Al0MRAf55L/83Lsuoq/JlAT3MUHOFwGOMjTk7tYDs28qFFq/r/8+3Ov5NPuq/mjiWt5pgTIClMTT3Lt2mEemkMXHyvrVPYtMvfUVZxX6xPIB/gm91P8S1aZan6M1x7cx8VZQrMu7XS84Z9VEPnhvYaKVElRH+VVxJ9duC4sfBqxv4cdrv5Lf44LfY8T5LM7dvvuZn3vtOlZh/4GvlJUfLGVvkenxUK30qWdXss63nCOnfNZGYZhbDNM+BuXNyoYhyOvNz8S8pCveky6226IEldILh7Skd0VnKv5e4amViFqH/fka/lHvUeNZCQ83LOEltkMrOPcRRHkDEdKFWqyjwvs47nd/h7BljP5G7ztmd/k7fLP+bnG6/jT0gyr00DtEnOlGpXJz/ClF2/j9AHgrL92cpVJOcdVZ1/A44J22i0A/E++jp/ge909HGOBBb6XH9s7w+qcf0bPo4uAZHWsaDGQ3NHoFuHXa+LfiXhuCySbohE71rhiKvxol+hKtzNSf+/nB3EDWEt1zv014C5X5X/2nholsnsjdOtQ3L17cYdbdbhmwzmc3M9h4N8Bb3NHbPfDMIydgwl/Y/uSP9rejeH6FIiESjZ57TgbRMIkLbKniDrPZglMUFGUFrhhQTFDJFp3xcZdJ/Ler6DiKm0xARX+00SLhHC/S3qbSOwDHOYLRWCPn/N5dJdhdomps2/nN+Vantp4O79RfCmfL1zL09OoPWj6UW50L+Sx8t0cW7mN07PozsJ+1M9/wwpTaw/zonNHub+CduAlPKu7+Ab3n3k393AMwC2wIN/Nj++aY2UXKvIX/Xi1zAWZCvt4XoM+o7x2n+T18ZyIkPjcpF0MhnnUx5rAvRNQm9Zk4tBRtwj8sqvyT8czqXz43Q7NdfkzXs1e/hQomPA3DGMnYcLf2By2g2jPQoVHWph3+7OKuXYLSZpgpYH2CH1gtU3UR/MK9o0K0eKggCbZhgo3S0Sic9HPK22BCf8Pc5ghad1ZSTdRu09uKe3mYgHgMF+oozsAu9BFzEU//j7g/Nfye7US9eKHuX3tj3nj1Bv5aJOoE28FWH+UG6cFN1Gifuk6nppFE4SniBKFr11mevURDl3Yz/nyNTxTQ8Vz+M40T/N69738RPE+bsW/5k5xXI7z27vQnIOiv+fz6GfU2f6hzzadC8BA3n+ReKfj9P9LdG7YFY5fsV2oOyHzvAB41lVbi8AdgdyPmOg3DGOnYcLfyGYrLTLDoEKsmyCHpOBLWjYiUdjtyzSJivFuVo/Ooj4534qfbzzqXkbF+TrBXx159pdaQlHfa/xzCdVvGkTvcZZkRHUZ5+IefgBOy7HiDTxeBHicGxrHuKfor92Niv1L6HO5GlgS3IU386HJSdbqd3Gi/ileMfUKPtNAn8sG0W4Cxzm1+JN8z4Eb+eIlP+YqKtKvA55GFxYlgHu55bndXCy8hAcL/v2HMqL49+Zu5a8TCwCAuzlWOMY9M+iORLANPY/69WvZDz/+5CSK3iqDLwaS46Z99+FzSe8apXGx121xYBiGYWwKJvwvR0Yh2jcj2p6HdkHWi86Jme0iLItJeidJruFc/mhkZA+KW3PCvUJCaRDjQegtp8bI6qeQtAc55xDZTbRDALoTsJo1rdNyrHiQs4UKNXmcGxrH3OkGInNEeQVLqOgvAteidppnTsip8hqTpQ/x99cEJ09z9eTVnN3w922iC5VZYPF5ds8WaZT+I9996STvKRGEv3NLiFxNsOnAQXQxUDrDkUurTLlbua9EJP4dScuNO8bdcg/HwmKnJaIdIuiiYy/Rbsfz/rz13N/j5HcvLtJHI8Q77xrFqz6FiktZ54TPPr91yTAMwzBimPDf5sg8u3Huf5989j03Lzy7kPcNj0e0Z5G/ck1Er4htPkEPKqAme5zTVdTLPEVXzYj06xxKJK054T1OoEJtmaT1o92/r2Nl7Vo0SNtGVOjvJZl4ebHb/O+S44Uv4xMlPXF38xZ3X93fbxe6MNH8AOeW/YLxOn/fpwTH3+EPp3ZzceOUO7EhQuEsB6YOcG4dFcgl1KMv6HNefZqry9fwzIzgLjikQlL4T6MC/Xl0UXMd6vufuJdblh7ixc0T3BUsQXGKRGLfneC35C5OhAh6eEYCOIcU0YXM7tjrl4iaqPWXoBvtCIV7BEYflW9PPM6iW1lXB7hdL7v4dUuF2bc0zxTeMdL5GYZhGDseE/7bHJmnALzBVfnjMd3/1cCGq/Lx6KCkS0n2oqtIknnuAE67M/JgzvGCxWQoUZ8HmcftaTz3tuf+Zt+fkHzPDVTE1lDxHGe5xy5EWtxli3w9fx/JHZDne1mK4mK/RsUdcfeHkpihYdcsuihZaY0lco1/7Umca5yQU+VJ1srv5x2rousZOcf+6f1cWCXazVj2c9+Fius5wV1yyFUf4s1Lt/PhGpobsEiopy9yHXAOtQdVUCvReaB4L7esePE/iVqHNkhG/8NuCYBb4CR3slDUx0XdJ1/6x4eLJZLujj3nUOUonyUoi+xdqZAPsnk/lKJdoE73aAIy9fKV22pSeUvj/tJ3pUdAP4fL/wenYRiGkclOFv55o747GlelCeMR/ToB9/OCW0KKb4gdHW31Eue+YdKtPQt8zke1J+kt6tdx7vmRzQGCME52Y3358n/9mqU/OE9UdjPOk12fgwrP9AIpdEeOiES+oEK5RNSo90IeW8eCLMg384EywFEq7rB7MJT7FL94CNHkWmLeIvtREf9UuM/t8qGpSai/333risi3ynFOySneNotagUCF/vP+vZXRZ+Me5cbWpPdxoYhzDUTAuTVEZv15a6jgb6Li/iIaoV+/lfsml5hdPcXxtRPcVQEmcW7VR8KbPpk5dBpuLnAnC9zZWOAkIgvxEqa+AotrAMvOseQ/iwl0EaBNvkRWiD7X/JYgPa990SBSiX1uo/fr625F5x0LvxO2+tnpe4B7SH73QmOzQmyOmXexykSGYRjGduSKiPiPG5lnD9BwVRZHM2BuUZ/pUx/B/Ssd7h8EZVpYNXGu93tP7oKEL2a8a26Ec07mOVx2tULt/okLJHcALuRtJrUgC/JO3lepU3IALbGv85kkat61hkZ6L8Ve342K4HM4twJwq9xbuo4ny6/i42sLbsGJEBf94RnsRyP0ISF62r82/fu8aeNr+YgDZu/mWO02d/cyIgdw7llEZr3dp4DmDzxF1EdgH7pjMAFs3Mst6z7yX0AXCVnfhWD/gVhDL8EV/J+bztFs2wXQ9172484RxLn+t04UuR9NzX79vsXnCONI3m3PL2hvoOVcPafdyHYNDMMwdig7OeJvwn+7ITKBRm+7sXmiPpqHkGxKFScuGONsBAGcY/xg84iLpzodRH5qXlcBUjxS/5RDys37i1f3IzJFkIc51PL4H3KPpGv+70GFW4kQYY8/b42870VzA1oLAY3yr9VPuRMb4T6+cs50a+GjuwPPAUVv25nyz2AV2CW4RYfsF9y5T3Lz1M3uUyttwl/H2RubW90/z+uBx9DFyOppXr8BcIx7Qv+DFb8bpImwGv1P23+itxktAJxzNPwCINiG0ouAMvp9KaCf4zpRknFj5Im02Va58fYA6J54DLoAbvjzeuch2K6BYRjGtmQnC/8rwuqzbcgn6msjt990Q5NFOyU5NskWMpGvPd89sioMZUfk0ytRkQIiB1JnncO5ZnOet6MCuqfoF0Ee4HAZ4AHgEI/WUguK4NsPEfQG6Z0D/fwOoh73x8P16Si/v1/hXm4p3MLHJ2Oifw8a2S8ADR/JLvnx8DafVsOrm/l0t1X5OrpTENl44AngBcDjwJ5j3LN4iuPaaVgTj2cQqfkFB0T2n+Dlh6isqXNIE71YfLdX51xkAxIJXnlXd44NYCX2nkLvA+0yG6+S1c93pxNZCwmRckbS+tYtBvT70K0XQqFHUn0zZh8r+EVZN5q2a2AYhmH0gwn/UbEdRX1ARW2nua2SXfpT0Pr3+e0aneq6R2IoLmz9GW1Cv4haYeJjPJslcFyVu7tPJ+qi+wBwmC+020O0FGfIIwgC8XxqUVACrkEj7I/HX7tdPjT1pZxv/qr7tlW43fviKZzhSOEIny3HIvS7ULGuHnGlTORz3/WrfNtFTajt+qYE5xzOrfhF2wT6vQqVhi6gdfmfB3ad4K5loH5ajhWPqfifQGTCi2/nBab460PEOl4P3znEAQ3Rop+tBYJz+rxEKATLu9Nk3xrJRUDodNxAFyrxd7QxMmG+HRcDcfSe3fNZ9DPsJOYbqe9lcStzDWSesqtm5ukYhmEYOwSz+uRBBcx0j7Pa68hvJd2tOXVUjM10eP1S35HDbiUaO5F1DxVl+4nETpO08O5rWpHYB99FNy1+VFyFUpXhPll9AAqo4Ad4Oj7OMTldvJHHJl7MQ6uxKL8A8gCHS4f5QrFlD1I7T9H788s+4h5KmC57y81uwV3ylXQOtEp5amJusPoIMJNh91F7VZS4G/z+oYPw4mle3wQ4xj2hxr2WEY1bf0IJW33fRZL2n+jzcM6lFgDx9x87jbhInfBjThF151079JKHX/x06ZqvXvvs1M+gwjZXbsZQtOeSaNLudrfWRAuYdB6MHjzq3gF83lX5mD+/AF2XkZBz10DmW3kur3ZV/qqfaRuGYVxumNVnJ5Nf1G99pD4LFZHpCH2IpC/519KJt9oMy7mLA94zu+Z5iLBKRtixk5hQ0bU3dqQBnB3GsiCC3Mstxd1cLPjIfrKzcHTiDJqQGkp/qrc+bT3R93MQfc/PpMXo7fKhqRdplH8lukRFv8OLykj0V4AKzl30771O1L9gDY26l89yoO7PD2VC21FRHj9yEc13qBGJ9CZa6vMG1O+/BMwe456l07y+eZrXF45xT92fN43Iin/28ei/Rqaj0pd14mJTRJyPPotQ9FNyYQcgPI+wEHAOF3vGK7FFwOTF4u5vXy9M/nPg/wfEv6b3GbRUaC+ydwZKGTsD2d+jcdFtUSQi4ppvdlL4U/DCP89CJv+uwQbwM+h3yjAMw9ihXDYRf5mnBCwAP+qqhIjo9o/UZ6ECpNO813Cu5m0eoYpO/Dd3bajEX40ShmozEd1Evr7eSeiH8o+BOs5dGHh+rWFV7O/nfBHgEI9mV5HR+YaFxjpR466LmcJIk2+nUHtRYkEQovxv5sNrx91dcZFbAHCIduGN6voXgL04dz5m4XB+Dtrl13cOFtyiczRDdF+EYlvEX8eMEnz17/Gov/jk0TK6EHgBKtQm/H/LpzjePMhZ8ZF/0O/ZektUpqP/ekwFcVSxBtp3AEIlm1YegH82YTx/ZvJ79a7r3rvv7pnbbvqbB1/+ILrAaPrPqUlygVvr+B3bLDr1i9hOi4HNJl8isuUaGIZxRXHrzeLu/YOtuZdcZ1V9MpF5vhz4yxdsPPatj33+hR/2h7efqA/oL9RZol+q8Q+i4ZMx0+fEWR06IhpZOiBZeaRdBMbp9qXREpi7YkdGltcQquTcwONFgMN8IZpr9jym0Lmv+T8ny3Emz9/l532BjMpEJ+TU5AzL7lfdt60nL/NiF5lCFzVR7X/Yj3PnCOUdVThXUEGr0f7I5jOo8A+7GJdQ4R8+uzKR4H/O/78ErCbEf9TzIdmMKwj8KNk07v9PdPptXaMLgJZQjC8A/LNqLQLiuwOxEyb9tRP4vgaERUm0EwBbZQnKwhYD7fROQgbnGjLPm4CHXJXPbf6kDMMwNo+dLPwvJ6vPvcC//GL5xrsYstPsSFExU4kf8f9Xa44KvwLqxQ7R+xJaIx76TbDtPI+4yA+4hO0hRPPjUf1edgG1HsVzC9Zx7uxwk40Pj5ziuHwZnyh5G0/3brH63ApEibSauOvccx3OnyHyxD+RXtQck9PFXVyq3ML9tZNuIS1kg+jX7sPJXZH9aKItRKU7Q8dYFdD62U8/wOFVOid0dick7GrkfgmRUOWoiSZuz6BCOixYJk9w19ppXu9O8/rCMZEm2hys4j9L9f3r3KQ151CxRkVeKOca320KFiAVeWr1SeQB+Gi/i9uA9PRWDfzw73YltgiYRSSUeQ0ViUpbYgnKImvBofOppI52XpRebvT6+aTfoyJHmj+IyO8AP7Y1EzMMwzDSXDYR/3Eh8/y0uOb1zfuL/4hs8bYes36EModpHLA4su3ySJzFx3MkS1P2Z9mJrpshKnsJKhSzo+gDEhf7AIf5QnKB0n5BqCEP6nvfRbSw6rQjEEpzrqBR/rb3fZvcPXGIR8iI8rd2QrzoX0uIHy3buexFqvr6NbIeEnqXoFVHP57UK8ABwZ0DcMgkWr0nHvGvoLsHa7H77UUXOUv+tUbsudSBG1HLjxDtgqyf5vWc5aA7wV0hqbfkX19KPI909F+PhQXVRq/vkkhrwenSkf7MBUCaqNdBhajXw4a3vBXQHZQwTv7uwZtJ9G8wzpWzGMhA5in4LuqGYRg7Gov4X9n8LVRYRZVxVFROoGJk0kcvtaPtoAm2Gcg8rxXX/Obm/cXvSr3USNk22iP50Fvk6zWzRPkGgtbwf2bwWXe6DXKSBb6ZD5R9ZL93JFdLcZZRW8iSn+cc8HyXnIMyKvgbaIS/TYickFOFNSYnjvHxrCi/JvGqLWeOUJUnOmEXuhDY8OIvXic/dCEOoj8tmPeiuwTia3q2iyQVu3OoVSgQFjszwDIiJZyrxxYeXwRuxLlHEVnzz6lyjHtqpzjOKY7LCZHg4dfnKBL5/rOi/6H8p74vR9SYKrL/+Pfn/ELEl/0sElsAxCsCJRZU8UVAlLOyElsETBH1h6i3FkJarpTY8fFYcPS+yXtrIu0VuzNgot8wDGP8mPAfBpGZD+x++99/tPzCMrArJjjWRx0F78BBh7yiTSCLl1Fx8kZBVbjGqwItj9K6k7yViv138r7KA5TcYb5ADrFfJMohWELFcQUVn51zCVS0HkCjsG2VegK3yd0Tr+KL8pPue1bh9rb5oqLcIczRHhnXhlVqnVGvexQRV7Hq3GLss9l1nFOLsVsUVVBTRCsBxbsFS8fPML6ICOU5o/ND9aILiFzlcw5W0F2bxgnuapziuAMKqO3HoYuHSUQKrc8jq/JPZP8JjamamQuaaAEQr/ufaAgWE/pxK5C05QIkn0lYBEwQVeSpx3bY4jX8m113jbYCWwwYhmEYY8asPr3ITrAN3uaVsYuJQS070fW70d2JwCLDVAXqeTsNZT/A4XKJuhziUchTsSVqVtVE/fi7iT6Dzp1gvX0GXRyc7bSwOCGnCgc5O3WAc2vpKL+fd/Dzh1r6i6kTKsBka8EX6vVHr4WOwBqZTlfz0fPaE3v1+BwqaKO/t98/WMia/h5Fkom+dXSn45J/PeSVrPrXOMXxyPYTzVvzJZILnPbKP3q86I83iPoBQIfkcP9dCHaYhktZfeI7AHpZRytQsDCB7gCF8qPxnYAiyb4TY7UEyTwvclUezn4x0ybU3e5mGIZhbBlm9bkcaE+wjbOYZQnZcoYV+TrGXpLJxhdHaT/KviXiHO5BOVzxNh5Qj3YvsS9oF1pQgbqIfkZ76FSOM3n9PtT+8ixdhN5tcvfES3mu8LP8i5Wsc7w9pemQIiru06K7CMzh3Hn/92DrCa81iaL9BaJGW3mjuiv+PXdekGmS7z7CIkrEtaL+wfLj3DOIvBD1+zeJnucKUD/IWTnFcbztx3lbkfr+RVZbzya77n+U5KnJrsQWHskFQLQD4MIzEKHkv92tBUBIBvavZycER/NZiY0d8hjKRBVnmrGFk+YLRP+cRtc9OAe+GdavA9ntX7J3BgoZOwO2GDAMwzD64sqL+KvgitfID0K/Sdq6MW76aYzVeYx9JOuhP981Qj4igkgLXXS92M/XHTWqUgPqX6/Qqxxn8vo5dEfgIl0+UxEK38L7J2/iofUOUf54Em8Jtd+ku/tGZTv171HpTv17hWghqSUqY9F+gp9dBfYunLvQFvHXceIJvjOkE4r1+F6iLr7rRBV+iAlgB9yAc4+15qqLoxW82DzFcQBOuFNhJyKU2Fxre5ado/9R+c9oDqGqUZLYdd0SgWPntMbpuAsQzSHsBMT7CTSJl2xNfkZbUipU5hFXHbCSU2uQVs+NOLYYMAzD2GQs4r8dicpoxsWzRhg3OcI9EKOJ5ocyknGbwIWtEgJB7N/LLcUHuFjIK/Zlnllg2Z2ROXTuNZx7Libg1+lUjjM5gSn0/S8BT3a7rxx1/233TRcX3v+Fd3y+Q5Q/nsRbAUodRP8+orKdEEp36ushqj8Ti/YnKi3FhOtetONubzS6P4dG7OOEJN8GoXxnFPVvxOZzobWQ0K/dCiqQV1Hfv5ziuGvtTuhzXEVkCpF64vvUOfof9/9rQ7GoJ0BUJSiej+Jcy/OflQcQ3ZKQKyBdFwHZOwEAErNGudQioEi8Z8AmLZKHFv0Qqiyl83tsZ8AwDMPoyI4U/jLP2wHnqvw3/ws8vI+4TWerEmz7J4qSAm2WhX7HuYpk/sH5rUwM7CD2k1WFug9QliONR/c3zv8EWt+7Cez14mUpl2BRobYPFbVPtUXCk/PV8o9H5PGLk3sudhD9oQFV048tpJt6RR2BL8UEbWTfiT5jjZTr34PVZZos286wu01RYm2jFe3XnYRQnWcDkQrOLSEyg8gU2iBMUN//pP9/4wR3CeBOy7HiMXe64a9fRavmTJDOy8iu+x/EadPbf0oEW010bqcFQBNoxvsBOJe0RqWsQAX/XWzrDhx7tmERUCBKYHdo5apwTZQzonOK579sqSVoIPIvBnon0huGYRiXHTtS+OPc+4EGUvg99Bf19uzOG8i27PQvIKJE1fh457oJ3c0gePYRKT3MC4s+Qbfel5DQBVsBqDspvP36+hP3E1XreS6XCFahvR8VbWd7PQcRrffuHOvA93U4Jy76tQxrdkM49cZH0f2odKdSJpTeDOI69pqQnUAd8gm6vo95pqdevvyjq/P8nKtyJvVyDY34az3+EI2Pvm9hUaB+f5HH/AIhXBvEfx0onOZYEzlWbHX5VQtRGS1Tm7T+dIr+62t1//0todWDGv7chODPWAA4oB5fAJCRCJzaBejeG0DnlbUIaKKlTEH/jS0nIv7a5GxLLUEjIXsxIJ0WAzLPzcCqq/L5rZmgYRiGsVXsTOEvcjPAtozoj8KyE40VSlBG3mQV+mOJOoogd3Os8DCPlJBHARqH3CP5rRAqjoN3f8ULzml3Rv4SXTgs9THOfnThcL7XroAXghWgnraNpM7Tyj26qAk5Be2LGV20NIgqxmh0P/L1q3jX81aIe9uj70d7qUqP3r/bO+KFqzJ1B/AgpIR/lOS74cV3g8jfT8vyI9JE6/vfAHwxJv6Dnx+gvsCdssDJ5lkOSizpd8Nfn0z6je7RKfrv/LyS5T/Dc4mf32EBoKe2EoGb6WcYF/rxRUDHXIDkIqBItHvWwC8C5l526XuXCrM3O+fe2rpunN2DR4F+FpmLATnS+K9OCk8AbxrL3AzDMIxNY0cK/4wo53gYociXefbONJcOL3127lGSNqCzQ1tAhkBiXXQf1vKbzb59zyqig9Bb9Md2++e3Srf6+8lxgrWpgvrhe5YBTUX5O52TbBylVpyNzAWFir1iatFZSp1bRBN5g90m7u3fhZbUjL47YYHQnSiKXeWzMi83AJ0aqWnXYk3abY/6R1V+NhB5jihxOESGJ4l+NgTx707z+sKxUOtf39cqGvnfaIt+d4/+B/tPkVABKTq/6wJA/9daAISGYGQt6FLNwVqVhbosAhpkLAJqUimT7G0RKhZFVYuSlqDe5Wm3I34x4OZ5A/r9NQzDMC4zdqTwHwuj8uUnxyzhrSpvfsHvvvSTU698BXDvuEVDXOzn7qLbPkjofwBaDWbVC71QnvMS/excaMWaGeA8WdVlMt4D+aL8yUZR6vdezbQN6ec1TTzROO6h178Hn/8sEJp1JevT+1zU2KFpQsWe+GldcFWe6vLyRTQpOnjzm60Ie4Qei/z+0zi34iP5a6hVqIn+jKgvcGdhgZPNY9wjRJF/hyb9Tvj33f65dIr+62vt5T97LQBi18fsPSERuC0PwB9LlwUtxK/PfsDRImBd5CQwgZC9C6Tf406WoPF1Dx4QVyX9XTQMwzAuE0z4x5B5BHiBq/JYZjR/WIuNiqO9sSMNfET/Q3AW+DM6BCM3GxFtRwuUH+AmcnXRzR5ogqiTbojuT3pB3cwd3Y/Gi5fm7FqpJ7qEMlDoFuX356VF/xzq626/h4rQPYSynXoslKuM23jCf82WYI1ebyX/di1DmU3+86Mk39WWoFehH4/6N1oCNfL7f9FfGxJh54CV3599U/nrXvj7R90Z+dQCJ90R7o9sPzpW8P1PI9LeC6Fb9F9fr3thXwJf/jN1zcKBk+6n93/n7AWRpdiY/o9ticAOtQG1PbOwCIjZgDonBEcXxXcCSn4HCzpZweLHdrolyDAMw7isMOGf5OU499NI4auB4aus6C/83bEjdZw7O9SYI0QEOckC7+R9Fd9C1OFc7fBgg835P9Vign8O/Y7lK8eZHG+anKU5o0sSUf5evv94Eq+gIncx8zOPynaeTx0rpCw+JeRDsskAACAASURBVKKE3hDtjzONcxeRHg5+ZdgVYA21HKm/XwV9shuszrWM2joeB65H/f5Nv9BZBOb+0fW/ciPwcTnqiu6MyAIn3SmOp8V/8P23J/1G9+sW/Y+X/4z8//5z/+Gj9X/YoPgOnHtTlv1H/5gvDyB2LiQXAenX2klafEr+ewqdktsvR0uQYRiGsWO58hp49UDmKblq7o6qqYtlkqgyDegv9v4i3JtMEDgPc6gC4CvyDCZAVDSGLqlLrQi37mqEY/15haPSnBtodZ9cNgkf6S32ivL7c+NJvNrAKt2NNzo5dA9OvpfgkY/+XiSK9pe8tSleTQe0adclSInLWHMu0eZdah+Kv/d4A6+8RA29QhfesFiJjxsWBg001yCyHfkdjQuFvbO33HTfroc//6LH/fFCK/LPXaTeo6B++Hbff3JuUSnP7NfD82zgnJN5ysABV+XJ1L3w43Rq0hbGyVwAZJyfrzlY+4VlokZ5+SpcJRuH7ThLkGEYxpWKNfC6jOhL9Gu0bzZ2ZH07RfQDQew/wOGyT9CFYaKNsVKchCRXtY3MoKIpXznO5Jjx0pzn8i4YYlH+xgCivwhM9hD9u1DPf1zka0JqkmLXaH+sdn9K9O8Fno+9F0danA+D2nBmCZ93el7xKj9aEWi2ZQ/yfQD2NZ9bfviBFzuEkvfhNxdEigucbAJJ733k+5/04n6j7+h/mJc+lDIizunzfzJ1TmSxytgB0L8mGoJ1TASOnZ8uC9rbCqQXbhASYnXOU/7a7CRxvSZuCSr77xXobocl1xqGYRgjx4R/P6iwnYkdWduOQh+8Z9/h7pcj5QeoSd4uul0GbC/Fqcen0fKPG33beaJx96GWlAt0EoqZl/YV5U9X7ikBFbr3gJhGn1lUxz8rUh28+6GxlT+aetZlnFshFlH2xCsCFZyjkcsIlI9LiOxGS2gW/WfWiP0Z/142fPS5Rtrvr/77IiHhVz38DZxrLHjR7m0/6co9a37MCUTWO4j/7t7/aG5J/3/2OL0WAHFBn9kROHV+phUo1y5A+yIg/LvptghI7h55S9DVX/L0l58tHny3OyP/sOd9DcMwDKMHJvy7oZHS6diRle0q9CEp9h9mpYA86o4MI/Z10PZSnHp8Nxr1X+1X8Ms81+Dcf/zYI6/7nr+tYvwCuluSV/D3G+VPJ/FW0Ah95xKaattKl+3EH4uLtFDDv4l2xc2K9ktsHun3uHleu6iU6Aqab7Hoo/5ZZ8cXBJHfXwnfn3XUw7/a8t+LFO7nSBD/SWuT7oCU6Cb+9bxe0f/g/0+X/0yPk2cBEG8IFnIeMhOBU9eERUN/VqDkIqASWwTUOlqhwgINOHe0cQD4uz3vYxiGYRg5MI9/HJFdJOt1L/eICI+dEIm8l1uK+zkf76I7uFUk+N5VlEc2F438hhyGS4PeY+LI+ltrhYn//qrV+15530O3fqafhUmI8gO1PMKrLVKrkVQhuxtvuKgMzLYtaEJkP/6PRsV+zS+Q6q0IdfKc3cAlwbWLxbS/39FAZAqX6uw7iMdfr5tBk0srJPMw2u1E8fen100RqhiFa9THXkB3u8JCqnCK4wCc4K524a7fJ90N6fWd6eX913NK/pzutrwcOQB6Wn95AP6afMnAnQeoQKsCUfd8CMMwDGNbsZM9/oXep1zGaBOpg63/9Bfw2dh/21L0B+vBaTlWfJhDFYdUbuHjHHKPrOPc+sCiX+ux7yIkuzp3yQvZSbT+/gzOPef/6/8eGomd+98Pv/Z/lZu12+77wi2f6lP0TwA4x3pO0R8q9wTRr4u67qI/lO1Mi/4i6Uhz6NCrlFqiP4vsakHx6zcH/Q5Po9WRZmNzkba56gKvFLuu4BcAkS0nsjJNxqLqzRPcBcApjmeN2/QLmYq3vnQ2M8UWE12eZR3doSiRrlSUPC9ZarXDeM7RiDUFK8aafXUZGpfaCUhUBuqJczU0j2IVfc7T/r/O78cwDMMwhuSKsPrIPO8FnnJn5KfRyGfgEs5dHNO0BkOk+DAvLPnIfoN+u+hmjxkvxXkpdnwW/Y4MX51IBWQRWL519d5GDU7nv7S/KL+/pkjcwqER+ey669FFBTTf4FzqeFbpTogSeuOdd5NedR9F7yAK9xIvEbqZaBQ/Xj6zTrr5mNJsWX6cO4vIobS1h6jBV1S607nmCV/tB11URPeN7rnqd1ym/JidrT8Qif9s73+8/GcZ/WyzF1E5LEB6KF9DsNQ1rXH8dfmSgZOD1AgLKl18h59RvXdIDMMwDKMPrgjhD9zhI2v/YWgBu4UEz74XW+GzcofcIwOLfZlnH3CrOyN3E9malhLiSMtXhuNLg97LjzeF2kNW6OTN7no5E+T08vvzWw20YqJ/mm6JlXpOARXiz2fMsdR2bZTQG9lmsiPKoXZ/IcNGIv0+jwEJSb7LKRtRssEYhLyAClrlx6E+/xuAx/zroR/ACmoHm0AXAqDVfgqgkf8T3JU1fmj2NYlId2Hby/sfzgmLlW7+fz23nwVAroZgGdeFHYBC63B/i4B4B+CwCBA0B8YWAYZhGMZQXClWn69E5LUjiY5vMi3LgEjhYQ5ViBJRa/6/Ycv8/WvgDz818Yqyt/OEhM8yWlpyDyp+nxvqXipa5tAkxUW0DGRuAeQtFxNolD+X/zmexBsT/TNo5LSb6A8NvJbbvNZZpTvjCb1qnwmLo+zodP+Mrq4PhGTRon9vxdTx9p8BGoEux865gMiBjOuWgXIsQp3w5p/iuL6PbEtRDbX+dLe2+N0EdBeh888r3aHQ9xeVxew2Zh4LkHOOuq/+U/RNwfLagJodFgL5UdveKpqErh2D9b8r5ee2YRiGMWKuiF8grsrHXJVPjHsenZCDKj8WZEGCZx8oec/+KMR+nPcCd9y89im1pqiveC8wQeTfHzwKrQuI0Ntgyc9/kCh/Ia+X31+TrNyjB+fQJNRujaQEFe/Jsp36Wog0p8V82Vt81KMfJcymk1qngdUO1XyIH/PCcDP9/jVCudFk91g6CMkGUXOvJeJ+fz0WxP8SGr1PiP/g+V/gJGTnEzTQnYLevn8/Zpe5xs/L5//Xc3MtAPRU6n4BKnkXAP66EPFvlQXtKxcgGigsAkKuhC0CDMMwjL6xXxpjRuZ5MQdpfv0L/sf+Be6sxBJ0e3f+HABX5RFX5RcQ2eUFf9OL/WEtPcVYTsCKfw/9Cv54lD/3YieWxKsR1qPuu4pHG59BFx697BHaaCmdyK0isNh2fTIhdzpxXfv7LYfPMaOazxTBIuOPxGvHZ8xzuF0A/Xyn/XziIr1T1F+PR5aYZ4D9KTHdJBL/FW/hCdfHxX+nZGJHSG7V6/OI/6juf+fznF8AOL8A6DVuPwuAdCJwrmTcsACIJQQXYjkB/eHcWmwRMEGUGDzanSLDMAzjsuNK8fhvC1qe/STPA+//4K63XtossR+bQBG1tIDab4b3DGvEcRoVqyuDWl1EqKASrC87lsQ78fr5TL18+aZVmTrcc+GhQlV3Otpp9/UrIaFXPf46Tqs5WB9Tn6W/8pwqeEdjJdKIeLQT0swcW6sUlaG1CHsCre8f/P4OEbXh6CJmEpGoGVXM848+G/Gvp+v09+P77930Kzo3+P9L/tzui8m0+O/y/XHtDcHIa0kLOwCx6xML176Il33VHYAg/jsnTxuGYRhXLCb8N5leET1X5QLwjk2exCQa2W4yyuRmjfAL8U6+fQ9BEf0ebvQjfGJJvHHRXwSmVgvT3wX8qx4DaE8C59qr6mT5+vV4JPZhkqihWbsA1bKoi10+/2xR1rnE5wr6GQ5TYlaTfJ276D873eWJEnazaLYWCZr4e554T4HI5tQk8u2HiHsYuwDIAifdEe6XE9nif8MvIio9xX80bvfE3+jcuj+37M/tLtDb+xB0XASkGoKFn6eNvBa1tA0odbw/2hcBQKsXhy0CDMMwDLP6bCbhF3l6m38LJzDr7TwFb+cZTelStRXMAavks9N0GIYKkZe/b9GfSuItoWJ8yVVxrkqvWv170Y7BWa+RIeTjHXrVK99jmmGMPj/3CtlJ6Kuo8B+ckOTbmmHCGuLI8ou3W36W0cj9dOycEPWvE5qFxRNs/XNY4E4JXX4zbSlJ339ei05I/M3j598g2gHo72dfbwtQSASug5YD7SehN8MKNFguQDRgsAKtojspU2YHMgzDMEz4byJjEvuCyF4v+Ne94F/peV2+sSd8pHiDIPgHiCTGvPz1frz8/tqsJN4yKph7R8NV8O0mu2wnRNVv0rT8+sTFeXZSb3zHYDRCKxLXwxKSe5fRcpy9x9f3HRfyZ4GrEmJbRbugC6ImWu0n/noTcEH8T7589aWlo/WvzLhX8P2X/Bi933Nk6enu/Q/n6ucruRYX0Zxy5QDo6a08gJAI3FdTrqwFwMCLgPA82xcBwy0iDcMwjB2JCf8hyPplLPMckHn+7hgmU0a76wZRO1w5zuTYFaKKLis4tzGodWDQKL+/tkC76J9A/fi9FzdR2c6VTHGftPLEj0f2m/ak3Kyk3hmcW+pYzUdr6l9qO74VREm+2VWGOkTC33H9+wqlo/Xvl/lWYnDw+8fH1t0BrY4ktIt/Bxr5r0nlZU0Kr+0ooqMKSxM9o/lh7DxlP+NzzVv+M3mPQRYArp9E4Nj1o94FiC8C1ogqA9kiwDAM4wrBhP/o+Q/AH8g80z3PHAW6fb8HTVJ93v83ml0GjYjOovaQVbRSz6DJu4VBo/zhelS6xEW/NiCLe5s7DyBohHsj00oTL83ZTnwXIEr6zSEwO+z4xHcPEnzjDb+xu9eYI2Stg2WnjffvecdMQ0o/gi4sg8hXv3+SkBOwAj5/I/6c/PeneX/xw+7+wo8ucDJtOSJ2bg1diJVzif9o/N6Vf6Lz85f/jK5xbYuArqfT9L0Amn4HoNSvgM+oCDTKRcA6UWWgyZ7XGoZhGDsWS+4dgg6i7gOoOOotRodBo8YFVJCPthvxiCr1RMNplLjfij2x65OVe/TgFNqltXclJBVmoWxn+85A1H23fUGSrN4zSzq5tj0RtH1HoOf0KAJNmecqdn/jU785z2tdlT/vZ4w+iSf5pqO9muibyttwVS7IPEVXjS28nFtGZAaR6dZz1WTfhh9j2eeCOESiXSKfmBva+t7JAq5TAq0m5jbRyL/aiHotbJOVfwrp99Lh/Lo/v+Tv0d93vksn4Og2mgisp1MSydcROGOM0VQEigZtosnj+GcQvhMO59ZknquANVdluJK/hmEYxqYgIo8Ai6jNuO6cu7XTuRbxHzGuyp+4Kv/AVTfB26+18oN/f8nbefoSmT3Gl+u+5MlveMf177sBFQJDif5UlL/vUqUxYZMW/dNow628Y1ZQX/5ih9ezS3cmE3rj3XrD4ijrM67g3HqPaGzba/79LQK/Bzza7c0MTTLJd514Q69uwrWaIS7V77+fZDJvEN5F/8wnSNfp9+c49FiPyH+T0LhKFwD5It2R97+Y0/6T9P/npU8LkF7S6gjcdyJwbIywAzZcc7DkoM3YTkANkSmcewT4k6HGNQzDMDab25xzr+wm+sGEfwuZZ0LmuWHc88hEk2r3orXfQ3fd4WvwJ+8xDcw8Vbr2uz6w+5u/xAuAgRcvIpSB0iBefn99e+UefWEGyN/NWAXcbMddkU6lO5W4JScqfxlIPR+Z590vOfz5r4xezvT3Z3XodQCuyrqr8vdclSc6vZ0RUkOk4t9fOfVat/KeWTwJXJc4Erfc6C5Jue0+MfF/Jwt6rJtwVjHqyOv7D/cI/1byVvLR8/uz/0T36l5atO2SwRqCpcZI5AIsPjD3Yvek/Fm/42QMrIsAkXcDPzj0eIZhGMbYMatPxEHgpcDj455IC7WWlAnVeTbnHpNEHv6mg9fRv/aIDUcBnXOIaA4yRnvlHn1h1s8z37gq2vYA7bX69fVQA759YZJM6E36/7Mq+Sg/+2Dl8CeBD3eZ1b6M+Wx9iUXnlvxiska6oZdrBeLzjhXq+x/0OwDheJNoZyRUlJFEjoXvA+CQ+G5K5wZa2uwrVPwh93ehn7r/YV6R/aeMfv6DLbZ7NAPT2yUaghX9sb7v5xyOp5aeAN4z0Fyzxqzya6MayzAMw9gUHPBHIuKAX3DO/WKnEy3i73FVvuiqfHTc8/DiZA+asDvacpzJ+4TSnHWcWx7Wx69DJqL8g4r+ds+yPpNQjSev6C8Au+hUtlPFe6nLeAWihN5pkqVCOwm51yDylp42n+3WTEkj6emkzv6i/vp8HFH1p3C8if6caQLraBWdSsY5DhFNWg1rqs6R/zpRszBZOLiQ1/rTX+WfMLcomTtf+c8s8luA3LCJwFzr1rnW/dFA8zQMwzC2G1eJyL2x/74945y/7Zx7FfAm4N0i0l4u2yPbTYNcsWhUMYimi5smDvU+2oAqv0e+x5DDR/lj46T9/AX0uSzlfiZ6zSy6cMpOKA5Ju9mLgjKaONz0fy4RKgfliBiLIB0TNuNdb6Pzi7meW8a1Q6HCfhZN8p0j/Yy7VzrqNOYNwNOky6WGhGEV/UX02W+kzhHQ0D+AC3q32/1FpkpHNn6sIaVHXJWf7GOerc7Pfb6/kp9T9+6/IyRm/0lWtTIMwzDGwq03i7v3D7bmXnId9/Xy7SfOF1kAlpxzP5H1ukX8x42W0NuLdp0dbTnO5H2KhNKczi2NUPSPIsrfKYm3iEbbF/sQ/YJWJKp3Ef3q6++8ExDZTvRzSVZoGu7z2T4r7WSS7xK6WEq/3u/PiKeBazOOh2pBNfQZlNoSaCPPfwFA8jwq51Zv2Hj8lw7VHv5gX7Psp+tv8rr+y38Oic8DaECrGtCW3NcwDMPY/ojIjGjwDtFd978DVDudbx7/cSGyi8hbvzn+fb1PqF/fRJs3jWhYBK2yMmyUv5Ofv4T2Jsg/515lO/UcFbKdrU1RQq9Wu4kWSJ29/bFTutgyNJ9iPXmoQ5OvrSNK8lVvYDs5POottPzmBUSuxrlnYscdIkH8a/Motf0ko+f+PEcowxmz/XSYwyOfP/RpRMoIEx0Xe53n25/3P8xx2PKfcXI+X/9vpOmrZQ2cB2AYhmFcVlwNfNC7SUvAbzjnOu5HmPDfSlRchAZNlwZOFsx/P7UOjVDw67CU8d13hxrnKD/BS/lh9zmSFXfUXlNO+erzEMp2dutrUGyzmET3jRJ6w3jpEqD5BFqnc+YyrDqFsYq3ZJLvKvGa/Pp6wz+X/HPU+v2ziMwkPkMV9VrpR6vFaK+IeGJxdF7Tn9cUocOKJHHPDb+rNdW2Q9P72njdf8kt4qNyoSH6n20dy4OIyFH3OuBxV+Wh7rdNJAKHn+GNfvoBGIZhGJcHzrmHgJvznm9Wn60gKsc5x2aV40zeb8qL/tUBxHOXYRFfl785rOjXAVmhTNrnrR7wfhOae5XtjM7p5s2OOvSqKI0EZP6kzm7nbW9h1s3a029Sq0b792bYeaI+CPoZl4FCm20m3guAmO2n2zx0/mvePtf/z7Z+u/5G14V8heJA9p+oBOgU7UnWXS7D+X4AdaA4aD8AwzAM48rBfklsAjLPfpnnvR+ee/MeL/hLXuxf3Nwbtyr1bDCiSj3R0JSAyjBe/jSuyg+5aqwTrlprhH6bkqnY2g1c6HFOZytHvEOvEi0C/Bm9ornbwLYzKNrJV6kRb+gFg3r9Qf3+17Udjbz14hemE6hoLqTO0862wfMfbFTdxb/zC4pK26IjD4NU/omurRN2MAZYeLgqf+iq3N/vdXrr1gJALA/AMAzD6IQJ/83hq4A7/sn1v/wSL/hHFnXPRKTiBb/zibsjqzgSi/Izkih/5xtNofPv7x6R6O+cFK1CsdBxl6W9K+8sJBYkuaO/O9JuEU/y1fyGStZZ/YpZOeqaZ4sHziNydYd7hsj6Eir+2wVzWvznifzrdWt+/Kz30pvBo//J7r+Dlv8ckFhDMGcLAMMwDCONCf/N4XeBbzlbuvreTb2LCotZVLSOrFJPNHwiyr955QvVVlPve/6hHKWWoey2C1Hq6OtX4gm96ao+ZPy9fzSZezHjle2yUKjFRHKjg0WnXxH7pqtfdvZlqA9+pu3VIP5V3C+jC45ym1iOiX/nYrs2vcV/zd87t32m7b6DR/8bfgFQHGjnYUh8x+s6sX4APCU38pQc2Oq5GIZhGNsHE/6bgKtSc1U+4KqbJOo0CqldfVXwjzQS76P8k7DJUX692QzaU6CbMM+6roCW7ezejyCU7uz8ejqhVxcS0eu57Ds5mixVOnxOW9+1NwtNAA/J4Cuo37ztrD4F8B8AH/cJzfs6COBQ6acJrKEFByqdxH/c87/AyTziX5t9ad7LYD/vBo3+R/dvaNWhrSn/mby95gEAjWZT/jPwQ1s9B8MwDGP7YMJ/p6GCf8oL/v6ql+Qa3gsvNjnKrzebBdb6tiap+FIferdnEJVozIzWyzy3Xf0lT98SS+jNbliVs1LLjrT5dMZliO++ov6uSiO2+H0KuKb9pJigjzryFsiyG8XOdQ53Jwtq/enVFVftN6sM6vsP9x6k7n907QZD+P+HxTlcoeC+CfhXW31vwzAMY/tgwn+noJVK1Hu+STkDcS//popYFWpzwEq/1Y0WDi4IWglmgnSpzXaKXcd37j3PFg98R+zIdOLZbrI/21dg2U6dWBe9JQnUetNuzxnA669XuTpwHpGDGa9F5TRVIIdci4kO57bEf+zY5vv+dYxQwrM4QPR/rP5/rnWXuHb0wQLDMAxj52DCf7sjMukF/7qP8o9ckHsP8ARQ24IovxD58vsSvTLPq+88cPLjaFnUbrX6s6r0ZJ3zVU4K3xY7P203ytVYqafNp7PAk221S6CitOT/nD2vwbz+4dpVVODPZrwWL/O53rpHlkgfTvzX/BwG8/2He0XJyYMsgsbq/zcMwzCuXEz4b1eiSj0NL/g3pe5/Osov88zLPHs3415eJM3g3OKAC5hPv3Ltkz9It7Kdeh9tONXjHq7Khqu2/P+TiTKifUZjewj4/cD5rMv6uQcxj/smshET26HJ1ijncQ7Y4xdaSZJlPjU6ryK9q/gHn5eSv+LPBpHvf/Cou/6bHMz7r9cH//9Y7D+GYRjGlYf9stlutFfq6S/pNfdtKHaI8n8G+PlNuGGJkJswIO6MTH7ioVd9pKugz67K02tuk9CWxDx8JZ842XPuVyyuoAnNm4fap0KSb1TmM3nO4LsUeu3TaIvxrNf1nir+1Zev4jprodAW+RdcvvlFvv9JRIoyP+DPwmEq/0TXJ+w/Mr/Jn7FhGIZxxWLCf7ugnuFNq9STvBUTQKGDl/9twMKIb1hGq9oMnpug3vPlHDsfpb6ThXVuUWWgnJV89NQtrcqzRh+dXUdEe0MvZfCov34+FzL9/tHrBf/nZfQ9NzNtMVniP0+jr+j61au+5NnbgMbA4l/HGbzyj17fwLn67pc9/xriPSQMwzAMY4SY8B8jMk9Jjrrffd2LPnYzmqy6KZV6WvdLRvkzdxJclVOuymdHeNMKKsZXhhhjBu1G3L3OvwrDfisETaOR9MTRfqLaQ/j0+7tuE/I7OrBI6OSrC9BO0fbB0e9Dtt9fiRYWuguhUfCc4r8fzpeuehh4P8P2VBg2+g9cKu7+FPD1Q83DMAzDMDpgwn+8TCJy+59Pf8V1QwnjHIhQoXOUf/T3m2fWR4oLQy1m1IYjPcdQkegGEKTFAXYIEnfu/qpMoCUqdw4h8TSivaGXkh2Fz083v38Q8+Fn1BLaWyC7nGZc/IePJGfCr6vyBVflHSPruzFE9N9VWXFVfmck8zAMwzCMFCb8x4irsgTsa5wpfmSz7hGL8m90ivKP/J7zHAQWr33pk69KJMz2PZCU0aTb7nkBKq4KfSdAh/KoyWOaUJrrcvJYguZw7lJf89oeREm+zq1+0/UfuL7Nex6V4Ry0yk8vv38Q7qG77wrq+S9mRtST1XZELx1Tg7Qo+j9Y5R/DMAzD2ATsF9KYcVWe26yxtzrKH2MR+NOny9c+NvAIKpZ29SzbqZT6ToLulgS8BTYfEXSHop9r5pEfuer79wxyv76JJ/kCv7H77Q8AH8w4T4X24Pfp5fePrDN6r3W05GjnSjhp8Z+32s9mEFX+6b/uv2EYhmGMGBP+lyGxKH99q6L8cVyVVVfl9a7KE0MMsw/nskpgJgmlO/tnjnQDsD6SekfBAIuGd/3A1T/yeZlna8R/HJEfB36ly+vDlMUMfv+5Dq83iMp81tE8jgJQ7nhfL/5dK893TJF/nctwdf8NwzAMY0TYL6HLjFSUfzt1hc2PyD7IsROiIqr/spvdFgs5o/15hKTMc+Q7r/lP2ZHswRYYU/7/W/XvtpXk66r8W1fltzLPGjbqr5wDdmfW7E/fIzThUjp34Y2Jf+cGyv8YLcPW/TcMwzCMITHhf5kgQmGcUf6RoUIzb8OyQRNzp9tKiw4Qic0RsT/zs/u+47/1O24Xfgo45Ko9GpiNivYk3+4ME83u5fdX4pV+1tGfX80O5UbDuC3bj+iewXgF9wgq/xiGYRjGoNgvnssAH+Uv7egoP4Rk21rPsp16bv+lO/W6CnRYGI0+Ilx1UrhnVIO5Kg1X5dFRjZeTWscofJxgxxkGXWicQ6Rbsm9c/K+ipUYbXee4ybYfmUdknkN9XRRV/jHvv2EYhrFlmPDfwaSi/DurZGQaES3VmKf0pwq/5oBCfaKt0lCf3v4+hOMrXJXv7DRM3vuNFa2oNNPzPH/20FFs/fwbXfz+yTKfmh8wwaDiP+wDDMfLgPfKPLv7usq8/4ZhGMYWY79sdiAyz40yj+NG9u74KD+Esp0Tbcm22ed2rsbT+9pJtCpMO30uIvIk5o6sLvxOIVhYhuc83f3+UZlP/fsyujipZ/YEiK5Lev7jYw2Bb3j3Tlfl4mADeFubRf8NwzCMTcaE/85kGfhd5lI16LcYmedlMs9Lhhukr7KdoKU7B224VWmzEY1BaOWs/7+dWERkV85zRxH1d8AzdPP7v/102AAAIABJREFURz758PmF8qPNPOI/8bk754aN/Lsqzwx6rZ9D06L/hmEYxmYzTNdNY0y4KueBt4x7HsCvo02VXjfEGPtw7lyuMwf19eu10+hc217pZ/dgRP5w2VG7NM7Vc3fodS7y4A93zw1EziFytRx1tcx+F841fJS86cX7MjANrCHSeYGYuk4EcUH8jxudW0j8HX8lIsMwDOOywiJLxjD8PeAbBr5aZD/krFCjYnIYIdRWAWjh4J0DCdQtboa2XdjoGklP0hxJ1Nq51Ve++BOvAS50TJ5NlvlsAquo51+6LlYyOvzKdvlYreuvYRiGsUnYLxVjYFyVpwa2OGjZzsU80fZjLzpdBArkK/GZda9ZSNqiZJ65Ow/8UHWgXIHhGX9kuV80/2I257mOEb3H6uT8s/6P3UrUNlsiX78jNXQ3U7ruPmR5/rcTkfff6v4bhmEYI8GEv7H19FG2U+a56Z6ZY3U56gb7rnZIBnZVFhHpyy419hrwO4uRWH7qZ0p/fseF9866M9LZDqYLjUaszOcG2qCtCBS7Rs2da5SObHyfzHPHNhX/TYv+G4ZhGKPCfpEYW4uW7QxlG/PwJPBuGLhc6WynakGuyuf6HWxE4nD7Ccx85E/yHaE3/b1PvmsZFb7drDuOeGJxclFZ7iaaG1L6EPDHo5ntJmFdfw3DMIwRYMm9xtah5RkrOJe77KGrsgr83ID3K8EOSqLd7vST5Kto1H9Qi1by3qt+p2ipyzlNL4wLPlK+5headVT817IWJK7K36SPhd2dbbULEC1uioi4MdnUDMMwjB2MRfyNrUEjrnP9iP4RMO1rvA+N2Xxa5E/yDV7/0UWol73473bPZJlP3VmqoOK/dwfi1jAq+Lfl5x55/0sW/TcMwzD6wYS/sVXsw7nzW3Y33V0YaTfjUUR/5Qiv5eW8YBTzGQv9JPnq+XVG9XNGFxLrvhFbt/OStfp18TeFLlom8t9O6+sMPuFNRBc4VvffMAzD6Av7hWFsPv2U7RwdEzi3tsX37I3wLyjwteOexpYzqsi0Ju52L9Wp50VlPvXvS+iCpdaP+N/WaNnPqOuvYRiGYfTAPP7G5iKyB7i0pX5kjQivj264Edo9hG901W0aRc6PJvk6dynX2dqUShtSjYI8fn8lnWMQdiuWEWnv4twDbfS1DT87zW0QosRm8/4bhmEYmVjE39g8VJyt+SjtVlLuV9R1QuYRjvCloxJ8l4HoD/ad/oMGo/WjLyMy0/UMtQZFZUX176G7b6OPhmQtwiJQ5rlD5vnKfq/fNKKmXxb9NwzDMDpiwt8YOTLPTRNH1n/ubPGA23K7jYrBvKVC8zAF/IrM27+VFP108g1R6NE9QxXxNUQmun427WU+m8Aa2t232c97SCX8/glQHXj+m4W+v6i6kWEYhmHEsF8Mxmbw5lph4l1Xv+zs9BjuXfQR6ZHgqqwg/C1XtbKgCfpN8vVXjTTq79zG5MtXT+LcEz3OC5HwIP7raCfgsp9T7t2LkPDrqnzeVbc8byUfFv03DMMwOmAef2Mz+EXgflfl2S29q8gcvX3ffXNZ2HM2h/5EfKizP8IGZuuFyYfIE3nXexcRES+MN/wipIhGyEujXDBuC5LefzfKpmqGYRhXMk+Ur+EHrv3HW3S3HxnpaCb8jZHjqqwAf7SlN43EjUXmt47+knwDocHWCHBVfhn45Xwnu4YX/00v/mux0qAySLOxbZvwG4hbnfS5D99MzTAMw9ixmNXH2PHIPC+4+aZPvtqXbDS2Ck3a7i94EBpsjYv2Mp9rBMsPFAfxxW/LJl9pIu//QO/RMAzDuDywXwDG5cAHPz1585+PexJXKP0l+SpuzOKzmfC+O7cCTKINscr9zG1bN/lKY3X/DcMwrnjM6mNcDnwHcP24J3FF4twiInuB5/q4pjlW4emcQ6SZsBw5txTLEakgUrtsPfGR97+I2eMMwzCuKEz4GzseV+Uvxj2HK5xBrC7NUXr9+0bFv6TE/yIic/7/EwzQBG7be/4DuqhpeO9/37kNhmEYxs7ErD6GYQyLJvn2gwrP8Xrjo5KX8Xks+cZzNS/+L2/M+28YhnFFYT/oDcMYjkGSfJXxWn4gaiwWxL8uSFbR7r59i/8dEe1PY95/wzCMKwYT/oZhjIL+k3y3i4deRW8xJv4bqM1nEn1flTHObutIRv+3f6UiwzAMo29M+BuGMTyDdfKF4PUfN9q8q5D6ewOooF74K0X8h+i/TB5Ze6PMc3TcUzIMwzBGx/h/4RqGcbnQf5TYe/13vfzS3tFPp2/SZT5rhPr++lq/ZUsBTfjdEbX+4zjX3JByAfsdYRiGcVlhVX0MwxgVA3XylaPuNcCfMf5kX4eIS1S5cW4dkSl0AeAQKfndgD6GxQXxv5NyABpnilvbfdswDMPYdCyaYxjGaBg8yfevgdeMeDaDoT73ZIMx51aJuvvKIAmwO6rRl2EYhnHZYhF/wzBGiSb56iIgF65KDfjLTZxTf4QGY8ka/yuIzKAVf4qIgNW+NwzDMHYYFvE3DGN0DJ7ku73wCa6J6jbOLQMzQB0V/wP//Nxxnn/DMAzjssCEv2EYo6YlamWefybzHBjnZAZGxX/6Z+QSMOsTf8tW9tIwDMPYSZjwNwxj1CwiskvmeQPw88DPjHtCQ5Cu9OOAZURmcW4dmBhE/Jvf3zAMwxgHJvwNwxgtzm3cPX1sCvhVf+TNMs9XjHNKA6NCPy3+m8AqItM4twb01d3XMAzDMMaFCX/DMEbOW278nXfh3D7/12ngfTJP39VwtgUq/tOVfhpoIvMUsI7I5LimZxiGYRh5MeFvGMZIkXluuFTc/X2+Ck7gOuCfj2tOQxOq+yTF/wZRd98aIkNF/i3h1zAMw9hsTPgbhjFq3ovWvY8zA/x7mefgGOYzGlT8pyv91NBk5hK6A1AZ5hYm/g3DMIzNxIS/YRgjwyf0fhXtwh9/7Ke3dkYjJlT6SYr/NVT4F4HGoOLfmnwZhmEYm40Jf8MwRsmvoJ7+LCpoou+tWzif0ZNV5tO5FaLFThORrIWPYRiGYYwVE/6GYYySrh17y65W+fYLv3DDVk1mE0lW+oEg/qdQ379DxDqjG4ZhGNsKE/6GYYySbwNWsl6YaK7xTc//eukXnrrjg4icRuQbh02IHRtZZT71eGjwVUfzAUz8G4ZhGNsGE/6GYYwMV+VjwO8DtfRrG1Jeec/ZH/o1YBl4PfAbwOOI/Bgih7d2piMgq8ynsuQbfG2g4n9nljE1DMMwLjtM+BuGMWr+Be2Wn+WmFP/lCza++E60tOe7gE8BVwHfBzyAyB8j8g92lD/euSYfY5on5Ed5yi8AdEGwgsiMF/+ljMWBYRiGYWw59svIMIyR4qo8DfwAGtkHcMAjwH/Rv7lLOPfzwJcBr0E7/K4CbwR+G3gMkR9G5IVbO/MBuYkJhK8G4pV+mmhjrymcWwfKiUpAhmEYhjEGTPgbhrEZ/CzwtP/zGvBOV6WZOMM5h3N/iXP/CN0F+E7gfuAa4N8CDyPye4jcvq298te681znbuFa10gcV59/HZFJL/4rw4p/0S4CtoAwDMMwBsKEv2EYI8dVqaOJvgC/5arc2/0C9zzO/QwwD7wO+HXULvS1wO+ii4CTiFy/ebPeBNTq4xCZ8OJ/qGRmq/NvGIZhDIMJf8MwNgWf6PvNwHfnv8g5nPsznPtm4Hrge4EHgBuABeBRRD6IyNfsGN+8Cn7xuQvrw1YyMvFvGIZhDMrO+MVpGMaOxFX5dVflucEududw7ieBLwHeANyF5gu8BfgI8CAi/waRq0c1301Du/uW0e6+Gzu2jKlhGIaxozHhbxjG9kZ3Af4E504ALwC+H00WfhHw79CSoKcQ+aptnUCrDb6C4N8oHm18VOZ55TinZBiGYVxZmPA3DGPn4NzTOPfvgZuANwG/g1bTOQ58FPgcIt+DyFVjnGVnnFsGpgHXpDALbN+kZcMwDOOyw4S/YRg7D+eaOPcHOPf1wAuBk8DjwEuAnwCeQOQDiLxu2+0C+O6+7oy8umfSs2EYhmH0QESKIvIJEfmfvc414W8Yxs7GuSdw7j2o9ed2tHNwGfgm4E+BKiLficieMc4yzTIis+OehGEYhnFZ8F3AZ/OcaMLfMIzLA+fqOPdhnPs64MXAj6C9BI4A/wl4EpFfReTVY98F0AZfq4jMjHUehmEYxo5GRG4Avg745Tznm/A3DOPyw7lHcO4HgBuBbwD+FzAFvBP4C+ATiNyByNwY59gAaohMjW0OhmEYxk7np4D/296dh1l+lgXe/95dXV1VvSW9JV3pbBAgIWkISwJh3wRCRhl0BBVGUecVZnSYS0SY8RJEFJ33fUHHBUdxH1691EFGEWURkSQkIQGCEjqEJGTrTqc6vaT3qq6u5X7/+D3VXV1dyzmnTp1TVef7ua660nXO7zy/uzpJ9/08v/u5n/fAlEMyZ2DiL2n5yhwh8xNkvpqq/v9DwH7gauB3gQEiPkrEc9oWH4zZ3lOSNIPNEfG1SV9vm3gjIr4b2JuZd9Y6mIm/pM6Q+R0y30N1GNgPATcBa4C3AXcS8RUi/kPLy28yTwIQsarZQ8d2/n2zx5QktdT+zLxm0tfvT3rvRcDrI+Jh4C+BV0bEn802mIm/pM6SOUzmX5L5cqr6/98ADgLXUtVIPkbER4h4Rktjgi4imt3ec3eTx5MkLRKZ+XOZeWFmXgr8IPDPWZ18PyMTf0mdK/MeMt8JbAPeCtwGrAd+CriLiFuI+OGW1OFnDgGriOhq2pA7+GKzxpIkLX0m/pKUOUTmx8h8EVX9//8EjlI9Rv0Y1enAv07E5QscxyDQR4R/NkuSapaZN2bmd891nX+5SNJkmXeR+VPABcBPAF8DNgLvBL5NxBeJ+IGFqMkv9z8GrGl7y1FJ0rJj4i9J08k8RuYfknktcA3wB8Bx4OVUm6geJeL/JuLJC3D3Y4AHfEmSmsrEX5LmknknmW+jegrwk8BdwBbgvwIPEPE5Ir6PiO4m3S/xdF9JUpOZ+EtSrTKPkPm7wLOAFwD/CzgBvAb4BPAIEb9MxMVNuNfE6b6r5z2WJEmY+EtS/TKTzNvJ/FGqpwA/DdwD9APvBR4i4lNEfPe8uvRUp/uOeLqvJKkZTPwlaT4yD5L5m8BVwMuAvwBGge8GPkU1CXgfERc0OP4IMO7pvpKk+TLxl6RmqJ4C3Ezmm6lOB34P8B3gIuCXgJ1E/B8iXlt3u87qgK9YsE5CkqSOYOIvSc2WuY/MDwGXA68G/hpI4HuBzwL3E/HfiDi/jjFPACsX4HRfSVKHMPGXpIWSOU7mP5H5RqqV/58HHgGeDPx3YBcRf0XEK2rq218d8NXU030lSZ3DxF+SWiFzD5m/ClwG3AB8EugC3gT8M9XhYD9DxKY5xpk43XfaiUJsZ3Vs59OxHTcES5LOYOIvSa2UOUbmZ8h8A3AJ8AFgN/A04NeA3UT8f0S8aManANXpvjP1+H8t8LryT0mSTjHxl6R2yXyUzF8ELgXeAHwGWAX8e+AW4JtE/Gcizp3m08eIWDfN6z885Z+SJAEm/pLUfpmjZH6SzBuo6v9/FXicqkXobwOPEfFHRDzv1FOA6nTfQSLWTAwT2+kBri/fXh/bsQuQJOkUE39JWkwyHybz54GLgTcCXwD6gB8H7gDuJOLtRKwrB3wNTzrg61XASPn1aPlekiTAxF+SFqfMk2T+NZnfRVX//2HgAPBs4PeongL8HrAdGCWil6pEaKL8Zx3wltYHLklarOwHLUmLXeb9wLuJeN9vbvwv7/7ohre/uyeH1wFvB94+tKJ38ImuTQfIPH/ShuAA3hjbuXKaEceAn8wdfLVFP4EkaREw8ZekpSLzxE9v5yPAy8h8xaQTgFeXr6lWUT0hmGwMuJHqVGFJUgex1EeSlpDcwUHg1US8Exgqm3xr+igwBPwM8OoyjiSpg7jiL0lLTO4ggd+K7dxExN8BW2DWA7uGgL3A63MHd7UiRknS4uOKvyQtUbmDbwBXAv8IHJ/hsuPA54CrTPolqbOZ+EvSEpY7OA48BvROf0H2Ao+V6yRJHczEX5KWsNjOCuAHgK5JL4+fviC6VuTYm2M7MfWzkqTOYuIvSUvbtXDGCb1DwOfLPwFIovutB//0ja0OTJK0uJj4S9LS9oNUZT7DVAd83ZA7uB64oXx/MmNFzz+s+zffRcT2NsYpSWozE39JWqJK+c4PlW9vBi7PHdwIUP55RXmd/Su3fM9g9B0g4lltCFWStAiY+EvS0nURsAF4J/Da3MGByW/mDvYDr6Hq3b9xzZWDK4HdRFzb8kglSW1nH39JWrp2AeflDg7PdEHp+f/bsZ2PAUfITCJGiHgBmV9uWaSSpLYz8ZekJaok9TMm/VOuPX1d5iEivknES4Bb6jj9V5K0hFnqI0mdKPMY8FXg5UT4d4EkdQD/sJfUFLGdrtjOb8R2zmt3LKpR5gngFqrkv2uuyyVJS5uJv6RmeRvwDuC32h2I6pA5AtwIvJSIVXNcLUlawkz8Jc1bbGcz8P9S/Zny+tjOC9sckuqROU6V/L+AiNVtjkaStEBM/CU1w/8Ausuv+4A/je02D1hSMpPMm4BnEbG+3eFIkprPxF/SvMR2ng58P9Az6eULgDe3JyLNS+ZtwNOJ2NjuUCRJzeWKnKT52guMAL2TXgvg/vaEo3nLvIOI5xKxksy97Q5HkhaTPSe28Svf+mCL7vYrTR3NFX9J81JOi303cLy8NAx8Mnfg4VBLWeadQD8R29odiiSpOUz8JTXDHwI7y69HgJ9uYyxqlsxvAOcQcUm7Q5EkzZ+Jv6R5yx2MAW8FEvhvuQPLQ5aLzG8BPURc1u5QJEnzY+IvqSlyB18FXgD8XrtjUZNl3gckEVe0OxRJUuNM/CU1Te7gjrL6r+Um80FgkIjt7Q5FktQYE39JUm0ydwJPEHF1u0ORJNXPxF+SVLvMx4DHiLim3aFIkupj4i9Jqk/mPuABIq5rdyiSpNqZ+EuS6pd5ELibiBcTEe0OR5I0NxN/SVJjMo8CdwIvM/mXpMXPxF+S1LjMIeBW4BVEdLU7HEnSzEz8JUnzkzkC3AS8lIjudocjSZqeib8kaf4yx4AbgRcS0dfmaCRJ0zDxlyQ1R2YCNwPPIWJdu8ORJJ3JxF+S1DyZSeatwFVEbGx3OJKk00z8JUnNl3k78GQizmt3KJKkiom/JGlhZH4N6CfignaHIkky8ZckLaTMbwAbiLi43aFIUqcz8ZckLazMu4E+Ip7c7lAkqZOZ+EuSFl7mvUAQcXm7Q5GkTmXiL0lqjcwHgBNEXNXuUCSpE5n4S5JaJ/MR4BARV7c7FEnqNCb+kqTWytwNDBDx3HaHIkmdxMRfktR6mXuBh4h4frtDkaROYeIvSWqPzCeAe4h4ERHR7nAkabkz8ZcktU/mEeBfgJea/EvSwjLxlyS1V+Yg8GXg5UR0tTscSVquTPwlSe2XeRK4mWrlv7vd4UjScmTiL0laHDLHgBuBFxHR2+ZoJGnZMfGXJC0emQncBFxDxLp2hyNJy4mJvyRpcclMMm8BthOxod3hSNJyYeIvSVqcMr8MPIWILe0ORZKWAxN/SdLilflVYBsR/e0ORZKWOhN/SdLilvmvwCYiLmp3KJK0lJn4S5IWv8wdwBointTuUCRpsYiI3oj4SkR8IyLujogPzHa9ib8kaWnI/DawgointTsUSVokhoFXZubVwLOA6yPiupkuNvGXJC0dmQ8Aw0Rc2e5QJKndsnKsfNtdvnKm6038JUlLS+YjwGEintnuUCSp3SKiKyL+FdgLfD4z75jpWhN/SdLSk7kbeJyI57Q7FElaQJsj4muTvt429YLMHMvMZwEXAs+LiO0zDbZyISOVJGnBZD5OxCgRzyPzK+0OR5IWwP7MvKaWCzPzUER8Ebge2DHdNa74S5KWrswDwLeJeGG7Q5GkVouILRFxbvl1H/Bq4NszXW/iL0la2jKPAP9KxMuIiHaHI0kt1A98MSLuAr5KVeP/9zNdbKmPJGnpyxwk4nbg5UTcROZ4u0OSpIWWmXcBz671elf8JUnLQ+Yw8CXgZUS4sCVJU5j4S5KWj8xR4CbgxUT0tjscSVpMTPwlSUvPQPwnBuJ3pn2vKvO5CbiWiLUtjUuSFjETf0nSUvT88fF4VcQMf49lJplfAp5B6XghSZ3OxF+StBT92IoVeSXQEzFLo4rMLwNPJWJzyyKTpEXKxF+StPT0Z9Kf45kMAV0RdM94beZXgYuJ2Nqy+CRpETLxlyQtaZkMAxFBzywXfR3YQsSFLQtMkhYZE39J0pKXyUlgLIK+WS76JrCOiCe1LLB6DcSbGYintDsMScuTib8kaVnIZBQYjmBNBNOf4Jt5D9BFxFNbGlytkvcywivaHYak5ckDTiRJy0Ym4xEMAn0RnMjk7BN8M79DxKVEPL1MBBaP4CouYSXZ7kAkLUeu+EuSlpVMMpNBZuv4k/kwcJSIZ7Qytjn1ZwLjRHS1OxRJy4+JvyRpWZrU8WfVDBc8Cuwj4tktDWwumWP497OkBeAfLJKkZat0/MkIeme4YA+wk4jntTSwuY0RYTmupKYy8ZckLWuZjACjEaye4YIDwL1EvLClgc0mcxxm2KAsSQ0y8ZckLXul48+JCFZP2/En8zDwDSJeRsRiSbhHiZi+TEmSGmDiL0nqCJmMl02/fRGcvXk28zhwO/ByItr/92NmArmIJiKSlrj2/8EmSVILleR/VQTd07w5DHyJKvlvf4195gi23pbUJCb+kqSOUzr+rIigZ5o3R4EbgZcQcfb7rZeL4gmEpCXPP0gkSR2pdPwZj6BvmjfHqZL/53NrvIiB+L9aHN7kWEZhmtIkgIE4h4H4HQbi7KcXkjSFib8kqWOVjj8nI1gzzZtJ5s1081+BP2h5cGea6VCvc4CfhGmeXEjSFCb+kqSOlskYMBjBmohp/l68iO/j3/FdRGxqfXTFTId69edOYAX9eazlMUlackz8JUkdL5PM5DjQGzFlM21/jnJbfgG4hIjz2xJgZXzaDcf9mW2IRdISZOIvSVJROv50R3B2//zMrwPnEbGt5YFV959+1V+SauQfIJIkTVI6/hBB7zRvfhNYT8SlrY3qlNFF0WZU0pJk4i9J0hSZnARGI1g9zZv3AN1EPKUNgY3DNCcPS1INTPwlSZpGJqPAibLpN6a8eT8wRsTT2xDaKGH7Tkn1M/GXJGkGmYwDg0DfWR1/Mh8CjhGxvcVBVZt5I1z5l1QXE39JkmZROv4MAj1ndfzJ3AUcIOLZLQ5qBLDWX1JdTPwlSapB2fTbdVbHn8wBYBcR17Y8qAj/HpdUM//AkCSpRpkMA3lWx5/M/cD9RLyghcG46i+pLib+kiTVIZMRpuv4k3kI+CYRL2lh/f0YEV0tupekJc7EX5KkOs3Y8SfzGPBV4OUtKcPxUC9JdfAPC0mSGpDJeCbHgdURdE164wRwC1Xy34rV+HFX/SXVwsRfkqR5KMn/qgi6J704AtwIvJSIVTN8tFkBuOovqSZuCpIkaZ4yGYqgJ4KesgG4OmU34kaq5P+rZA4uYAhjRKwkc3QB7yEJ4AHgTe0OojGuEEiS1AQl4R+PoG/Si0nmTcCziFi/gDcfx7/TJc3BPyQkSWqS0vHnZARrprxxG/B0IjYt4O1HFrysSNKSZuIvSVITZTIGDJaOPysmvXEHcCkR5y/QjRPIFrYSlbTEmPhLktRkmWTZ9NsbMWk/XeadwPlEbFugG3uol6QZmfhLkrRAMhkEVkawatKLdwHnEHHJQt22JWcISFpy/INBkqQFlMkJICLomfTit4AeIi5bgBuOAvb1l3QWE39JkhbYDB1/7qNanb9iAW7poV6SzmLiL0lSC0zu+BNBlBcfBI4Tsb3JNxvDVX9JU5j4S5LUIhMdf4DVpzr+ZO4CniDiWU2+XXWolyQVJv6SJLXQtB1/Mh8DdhNxTRNvNAbY2lPSKSb+kiS1waSOPz3lhX3AA0Rc18TbuOov6RQTf0mS2qR0/MlTm34zDwJ3E/GSphzElTmOq/6SChN/SZJqNdD8/viZnARGIlhTXjgKfBV4WZNO4R0lorsJ40ha4kz8JUmqxUCcA4wxEC9s9tCZjAJDEayNIMg8AdwKvGLebTkzE6BJk4jONRAXMRCr2x2GNB8m/pIk1WYFwH0PPnXjGYdxNUkm45kcA9ZE0EXmCHAT8FIiVs3x8bkGHwGW/qr/QDyJgehtw327gJ3An7b83lITmfhLklSL/jwI9D7tRff9PbAignMimp9Ml+R/VQTdpTPPjcALiOib/ZNzD000v1SpxR4Efr6VN4yga9OV+7eWbw+38t5Ss7nTX5KkWvXnMEAmQ1SlOWtKbf7R0qO/KTIZiqAngp7MHCbiZuCFRNxV9gA0MuhIqfUfb1acLRXRzf28njXc2bpbcj7AE4c2PQasB4636t7SQjDxlySpQaUfPxGsKwdyHckkmzT2cATdEfRl5hBwKxHXEXEfmU80OOw4EV3lScLSUU1Ygqfkp1pzOzYBq4C9pyd0DU64pEVkqT/ykySp7TI5mslhYH0E5zRx3BHg5KSOP7cDTybivAYHHGOp/d1fnUMQZJ5c+FuxPoJ+4HgmA818iiMtBkvrf35JkhaxkvwfKfX/65o05hgwWMqKgsyvAf1EXNDgkOPz7hTUKlWcXQud9EewOoILgPGS8J9YyPtJ7WLiL0lSE2WSZQIwWCYAa5o05nFgden48w1gAxEXNzDY0lj1rzYid5PVvoqFuQXdZYW/J5PHysZqadla/P/jS5K0BGUyViYAJyM499TpvPMb8zinO/7cDfQRcVkDQ43VdajXQGxq4B6Nq84c6CnnGSzA8ERJ+M8tK/wHF+I+0mJj4i9J0gLKZCSTQ8B4mQDMqw996Si0IoIeMu+latN5eZ2DjAO1Heg1EM8H9jIQ19Yba0NOJ/1DCzM8W4CtwJ5M9i1WiHkDAAAgAElEQVTEPaTFysRfkqQWyGS4TABivmcAZDJMNZHoI/NBYIiIq+ocZqTGVf+Ly9m/9ZcVNaZ3IVb6y6SrHzhUVvmb0n1JWkpM/CVJaqFMhkoJ0KoyAWiotfakjj9rydwJHCTi6joGqBLfaoV9Np/45j3PeAfwN43EWZfqkLKmJv0RrC0J/8mS8I/Me9CB2MTAnL9v0qJj4i9JUhtkcrxMAPrKanTdfyeXjj/HI1gb5B5ggIjn1jHACHOd6dOf41d/112foj8X9uCviF5g+NSEZN7D0RPBVmBFSfgHmzEuA3EO8ADwM00ZT2ohE39JktqonAFwCFhbngDUtZJcOv4cA/qCfAJ4iIjn1zNE6aAzm+EIeuqJqy4RPcDJsvdgnkPRVVpzrs1kTyZH5h/gqbHP+9Xf+rkrM1kD1LevQloEokkTa0mSNE8l6T8HGMuk7pNiS+egsSR6gKuBW2taQY/oLqv/s8W1LZNH642phnv3AKPNOE04gvPLL/c2q4Y/gg3A+vLtvkwGGYgbgNvpb/gEZS1h0XdNctnXWnOzu+POzLymWcM1VFcoSZKarySrh8qq9TnAaGnhWevnhyLoCXIkiTuBlxFxUw3JfxLRNVPynUnW+ySiJhGrgLH5Jv0RbAR6qBL+Zkwg1gIby7eHMnnkjAv689PzvYfUDib+kiQtMiV5PVwOmDoXGC5tPGv57HAEK4PsSeI24BUl+Z85Ic4cPZWEt0rVUSjJHG18CNYDa4CDmcxr9b2UMm0t3x7PZOd8xpMWI2v8JUlapKacAXBOrXX2mYwCQ0GuupGX3QS8tIbWnWNEdM0z5NpErARitvKi2T9OX+nUM1427jbUCag8Wbk4gkuADZk8Ur72NzKetNiZ+EuStMiVMwAOUx3cdW4Eq2r4zDhw/BXc2Pdyvngz8KLSLnOmD4zRirygSvq7yDxZ/0fpLgl/b0n4jzUwRkRwYUn2+4FdJdnfU+9Y0lJjqY8kSUtEKfcZimBN2ch7vKzuz3R9AsciXr76Su6+9Vtc9XwivkHmTBuHx4hYOZ/ym1lVTxRW1ntAV9lfsJVqz8NAY7fmfDh1avLu2X7fpOXKxF+SpCVmYsNvOZyqCzhaVvhnun4w4sreIL+axLOJuJfMg9NcOD5Luc9YBCsbTpirlqHdDST9W6jylT31duopm37XAUm18ffxej4vLXYRcRHwMeB8qv/Ofz8zf3Om6y31kSRpMRiInyuHQ9Usk2OlBGhdKQGasfNOqYPPIL8BXEbElhkuHZ1hP8DjcKpdZn2q04F76kn6y8/TT9VVZ2Ai6S+TncsimHbPQgTrI7iklPKMlDKenY3uA5AWuVHgXZl5JXAd8FMRceVMF7viL0nS4nANVYeaw/V+MJPDE2cARDA+06FVmZwsHX/uSeKppaxnYMpFSUSVrE9qA5rJWCOnCxMRPI3V3Js1tSWNYA3VKv2RsrF54vUtVP30j2fywJTP9ALnAUH19OPM9pvSMpXV/78D5ddHI+IeYBvwremuN/GXJGkx6M9/N5+P13oGQCajEYwH+Z1hVl26qkr+d025aKSs+jfUdecMn+GpXM29DEQf/TOv+JeORRupEvs95bUALgK6gf2TE/4IVlIlOAAnbL+pThcRlwLPBu6Y6RpLfSRJWkYyGSvlPydLucxZnXwyGc/kWA8nHx5g6zoinjzdUKUuv3ERff/46HftGR+P9wDTdvEpE5ULgLWlpOdI6d5zGfBkYCCTByaeakRwUQQXU5Ud7SylPNbua7naHBFfm/T1tukuioi1wCeAn87MaZ/4AUQtJ3lLkqSlqayk91IdAnbWinsEfQ/ypIufxMNJ5n1T3uye3Gs/gosy2TV1jBlu3HsD/zD+GW4YnWnjccSp8py95XTgc4DNVE8adk2q699KdTJvUnXkad1BY9IU0XdNctnXWnOzu+POzLxm1niqp3N/D3wuM399tmst9ZEkaRnLZBgYLodenQMMZZ5efc9kKOKhh+/geZc8L+JKMifXBicRXbOe+judiJ438VfMlPSXbjs9VAn/WARbS23/kYlyngg2ldeCqqPPcN0/vLTMRbVx/o+Ae+ZK+sFSH0mSOkImQ6UEaGUpAVo56b3h5/OVhz7Ha4aJuHrSh0Y5M1fI2ToHARNJf3ycNw1PTfpLx51+YBDYA1xcSnqOlYR/uJykezHVE4qJUp7WJP0DUdPJyNIi8iLgh4FXRsS/lq8bZrrYFX9JkjpIJoPAYGmLuZJqlX08k5GIzz38Z7zl4rdEPIfMr5ePjE9a9d9L1T1n+pr6iFXv4f/p+jhvGprcc7/sMzgXOAocpNqUm8AjwCrgvAg2lVjas0l3ILYD32QgLqd/SsmTtEhl5i0wx2R8EhN/SZI6UCbH4NQqfFAl3WMRf/7wEdaPvjVWP391Dt5B5ljp8DNW2oGumnbAKulf+SHeMzSpNr8b2AScKF/nAcPATuACqgnAiVa13ywTnfVw1kFgccsnX7jpRdfeBkx/PoC0HJj4S5LUwUoXnSlnAPzurl+L1SM/HhtesiEPfgkYKz3/pz+1N6L7l3hf94d4z2DZpBtUXXfGgC6qNp2HqQ4bWkk1AdhZ70m8cynnDEwcgjaxCpqTfj0KHJz+vrfexEBsoD8Pnf2etDyY+EuS1OGmPwPg1/Z8MPpO/kSc/6rz4Z+ZqZwgYuWHeVfP+/ml4yXp30JVvjNRLz9OlXCvAR6dqcNPLWZJ7Jn060MNTyhM+rXMmfhLkiSgOgMAOFz66J8DHzxxklW3/wR/8JqLePTzVcnPpJw6ouvDvKv33Xz4ONUTg41UucVa4EC5amByF6HZTDx5oErqZ0rsD89n8iB1MhN/SZJ0hkxGqCYAPb/ML/R9gVfd/jF+5LWX8eCNr3/FJ/sYeMMWLuDAh/jZNe/hQ+PAlVTJ/hiwj6rX/lmnBpfEfj2zdxU8Yp9+aWF4gJckSZpVBL397F79N3zvC6+651vvWrP++LZfuO4DL/ngrl94KlXC/yhVGc+hCNZx5sLi1JX7o5lMv1dAWgIW2wFe9XDFX5Ikzao68XfbiUe/tntw9ZrBF46OrIzL33PfL/MOfh2YOPArSqnPsUyOtjFcSTPwAC9JkjSjsuH30ggu//U/eNfFGQEQz/3WV869nWu//5N8zzPP5eAoVZnPKNAbwTnla/0cX6vnPBBMUtNY6iNJkk4pifg2TlcF9FJ15hnKZNeRb6//w3FWXH3eM/e+9aKRncc/ytueeTn3XdHLcA7Rd8fF7LqFGpOL0ue/b54hjwODbvhVqyzlUh8Tf0mSOlwE51G125xICnZT1e5vpirdGSgXrrhk9UOb2RBrdu6+ZC3wyEpGVl/GA8c28MTKP+VHr97IwWuBGKLvzovZdSOZC5qQR9AFrObMdqPTdQSacYhy3XE3FasWJv6SJGnJiOBc4NxJL+2b6MJT+vCfS9UPf9+UD64IchPV6burgXXALmDjc7hz73HW9A7T07WRJ45/mhuevYLxlwIrBlm94xJ2fn7GA8DarDzlWMP8S6AH3bi8/C3lxN/NvZIkLXMRrKY6LXfC4UwennJNP9Uq//5M7p9mkPh+Pr4KGKFaJR8HTlDlEvu/znM3AceSOHodX15zHbff9zBP+pdMRg/EpmfsZcs7IrasHKLv/ovZ9Q9kjizID9uAcuDXsfmMUSYPfeX3uq6PToRRfn0ik+FZPzEQVwDn0Z831x2oOpor/pIkLTMRrAQunPTSUCaPz3DtRVR1/Htm7cYT0RXkxkz2ldN9u4GDwFMz+XYEq6hWzcnkIBErruPLa+7jaXGQjadWwg/GhstHWXn9Csa7j7F2Vxdjn7wwHz3RlB98GYigl+rk4xmN7uq6s6tr/Cn0pxuj28AVf0mS1DZltflCoKu8NAo8UlayZ7r+0nL9o1W7zjmdMVYm+8vegIEItmWyu2r4Q3cEWzNzz+1wlIgVL+ZLqzfGlSsOsnEo8+C9wL3VheufdILetx2ITauOs2ZPH0N/syX3nXXwVycp/y5m//cxMP5WzpzYSTUx8ZckaQmK4HzO7Ijz6Fz15RGsAJ5cvn245nr0iBWcmfifLCv8ZHI4gq1l7Inynf0RXAA8npljt8AxIrpezJf6Il7cR/UEYmR9HnloPfwWwOro23aE9T+6L7asPkHvviOs/9ur8u5DNcXXafrztnaHoKXJxF+SpCUggg3AORPfAo/PVL4zzWe7gSdR1eU/2FDry8ycqEbPZCiC9UCWhP9+qpKfeyMYAbozeSyC/ggOZzJI5tgtEceBFZMmAKdKgPpyaHcf/E4J+Ly9bPmBfbFl/UlWHTzApr99Zt61v+6YJZ3BxF+SpEUogjVUG3InVtoPTd2QW8MYfVQ9+UeB+2cq/ZljkChjbQCmrsDvB7ZksjeCoxGcm8mhCEYiWJXJQAQby68Plf7+ExOAuIodfRFXraY8ATg1aube8+Cj5f4bexh+w4HYtHGE7qN7Oe/vnpl3DdT9c0gy8ZckaTEoq/IXcrq7y2AmDzU41jrgfKoOMd+ZZ2gryBwjWHlGck7VDSdOPwV4LIIrqCYoOSn5fyKCtRGcl8necnECeXfEIKcnAKdKgDjzJk9shj8uP9g5Kxn9t/tj85YxuoZ2s+3Tz8mvPzzPn0/qGCb+kiS1QdlgexGne8ePUtXdN9xuL4KNwCbgaBMS/gkzxXOqo0wEUeLeGcHFmeyckvwfi2A4gguBx06VGp2eAAwBbGWgN2Lr9BOA6vrDm+Fj5aZruhn5nn2x5Y1JnHiYSz/7vPzK2W1IJZ1i4i9JUotEsJWqdeaEOTfk1jjueVT1/wen7cHf+MBdMOd+gP1UJ/zuy2Qwgt4IujMZmZL8n4xgN9AfwRNndBIqp/vuiTgBxFYGemadAFSfOb4R/rLE2dfNyA37Ysu/TWLkUS78wnPy6zvm++NLy42JvyRJC6SswK+f9NLjmexp4vgXUJ2gO/2hW82QmaWDz7QJeCbj5enFhPuBy4Fvl/czgtGJyQDwWASbIujJ5PCUwSYmAMPAya0MrJpzAlB9bmgDfAKAiFU9DL92X2y5PomxXVx083Pzzjsb/fGl5cTEX5KkJolgLdXqN1SlME/UuyG3xvtcTHXI055MHmv2+OUmwekyn/WZ1NRVpyT6T0SwKZMD5bXxiGofQ3kScCCCdRFsyWTfNIOMAeyJSGA4yJ6yUXn2CUD12ZPnwKeATxGxspcTr9wfm98FjO9m2+1Xc9fteHqpOpSJvyRJDSor4ds4vSH3+EIk+uVeQdWSswvYVeOhW/O65cQK/AzGI+jKZAzOSvT3RnB5KenJ8trU5P9oBCcj2EZV9392Mp45RkQkcaIE1FvzBKD6/Oh6+EfgH4noWs3gi/ez6WeIzexm251/w/fe9Iv5i04C1DHCSa8kSbUpPesv5PSG3BFmSlqbe8/6D92a302DSYl/BJunrviXici6TI6U70937am+7wX6p3YmKj9P10TiXsa5gKpcaXjOmKrAeqkWL2ubAJw91ooT9DzvKOteDMQett71ba74/Bvz4/Wfb6COE33XJJd9rTU3uzvuzMxrmjWcK/6SJM2gJKX9VGU1UK3sP1pWuRf63quAS4Ax4IGFnFxMd/s5VvvPaOU5w/snIohSyz886fWpK/8J7I5gc9kEfHSGARNIIlaUJwAZZF9dTwBOjzXeC7f3wu1ExLkcelY/Az+9L7Z0HWDTPd/kGZ97Y368/gmFtMi54i9J0kCsAP4J+I24IG8F1k16d08LympOiWA1VfnQyUweadV9pwSxYtJq/7lUJUxnJcIRrJ+04r8KWJPJwSnXXJFZbfSd8voZK/8T4wGratpPELGC6gnAeHkC0E2jTwBOjxnAlQfY+Bqg6yAbHvwU3/Ppd+b/aNm/fy1+S3nF38RfkqSBeHom3xocWn3b2qccf91EMttKJek9nyp5fbTV958UyAogJzbATlfmc/pSzpncmWdquU95bSPQncnj03y+C1gxJfnvpTqLoLYSqqrlKFQTgD6qaoYTmZyc87Nzj/3Ug5x7/Rhdq46xdtcXecWnfyz/5Ni8x9WStpQTf0t9JEmCb0fwR2tWD/7JgiX9A3EF8Er6839OfrkkxhupDt1aHAdQNXFVsJzce3kE+04d3HX6vbFS9rNyYu9CKRF6DLigfGb2BL50ACKiK4lhMgcj6CsTqfk9Aci8f0PVnpTNEZe+gb/9kf3xqb4h+vZ8mhv+4e350UMNjy21gSv+kiS1wkDcBlwHXEV/3jPp0K0nJrrhtN2UTb3VS3Wt+PfC2f35SxnQxTOdJlxW/mPqxuXyezSYSW2r7FX81cbrzLFS/99Ns54AnL7PtiOs++5hetadoHf/l1df9w9v+s5fvxP4Y/qzWScma5Fayiv+K+a+RJIkNcFO4OQv/fr7eiN4KjCcyf2LJumvTE36Zzy4azplL0TPNK+fBE6Wcw6m+9wYkBFnViKUsqGuCDbVGECWJwDjE08AyhOcrgjWl59n/jJ3r88jH92S+z58EY9+5oYtn/5B4Oe4mf9OxIam3ENaAJb6SJLUAj/8jo998Hte86m/fv+Hf+m+TI43beCB2ARspz9vatqYp815cFcEUUstfiY7I7gignunu76U/XRNLvsprx8upTv9mQzUFHVVzjBGxIqqCxAnyByaVALUvCcAmY+vhd9mIB7hFr4J3EDEucAR4LNknn1AmdQmlvpIkrRASjvQJ1M9Yd85a5/6Rg3EZqrE/8Z5jTNlU2/10sxlPuX9LqBvcilO6UrUNV1bztIhaPVspw3PUvbTBWwF9tZdt1/9bFB+vgUrATrznucA11Od5DxINQmobeKiRW0pl/q44i9JUpOVJHXi0K0HF7Tvf3/uB25sylh1rgaWVfoVU14bLLX5ZyX+mRyK4Lypq/rTjDndyv8YVb//8yM4VtdTk4nypVY8ATh9z8PAX5X7rgVeS0Q/cAL4RzJ3NvV+Ug1M/CVJapIph259p8WHbjUuZjuKq+keBJ4EM3cwKsn/ygi6pk6aMnk8gnMj2JjJE3Xd+fQEoIsIMnMIWNgJQHXfY8Anyr37gFcT8XrgJPAFMh9o+j2laZj4S5I0TxGsAS6gOnRrcbTkrM9ZJ/WWspzDM1x/5mfPNhjBmulW5TMZjWBwakegGa6bKfk/FMHqCLZmsqeGGKcOPkZETJwB0LIJQHWzIeDvAIhYBbyKiNcBo8BNZN6zIPeVMPGXJKl+A7EV+N8f+eOf+pF3vPcj3VQtJ5diwj/bav/KRnvgZ3KslPtMW46Tye7S2//o1N7+U66bLfkfjOBkBNuAx2cqHZolyIkNwBMTgJw0Aehd8AlAFcNJ4DMARKwEXkbEK4Fx4DbgrmaeqaAmOfEY3P2BdkfRENt5SpJUvx8BXvKG1/3tm0tLzt3tDmgezlrtb5HHqJ6SzKok9FH2TZz1Xvm931KeutTvdAvQLCVAKzI5sSBtQGePY5TML5D5O8DvA+uBnyTip4i4tsXlWFqmXPGXJKl+fwn8yIX9u/+k3YEsYsMR9Jbe/mfJ5GgEWyPonuvJwmwr/+X9gQg2RrAqk4MNRXtmC9AuYLylJUBnxjIGfAn4UulIdC3VJCCArwO3t2mypiXOxF+SpHr1505ge7vDmLeJFp5nvVzfwV3TKf33z4PpE//iAeCpwL01jDdX8v9EBGsiOD+Tx+cR+BkdgDhzAtCaEqCz47kDuKMk/s8C/mOJ7W7gS2TWV+akjmXiL0lSJ5u+hnzOg7smOTHbyv7st2Y8gkMRbKrlBONJyf+K6fYGZHJ8Ut3/nnm1UZ3SAYjMsfIznmj5E4DTMSXwL8C/lEnAlcBPlEnAd4B/JnNeEzYtbyb+kiR1oibVjGcyPJEET/P2SCm/mTE5Lu05L4/gUC2Jekn+u6tcfNrkf4Sq3/8FERzMZKiuH+jsASc2AK8o34+XMdvzBOB0XEm14n83ABFPA368bBJ+BPg8mc0/ME5Lmom/JEmdacE39WZysJT77J3j0keBC6kS1lrGHZkt+S/XPBbBpjLxqKUt6Ww3TKrNv6c6AJUJwMQTgN4I1gHDLZ8AnI7xPuA+ACKeBPwwEb3AbuBzZA62JS4tKib+kiRpvub19KCU6JxfT8lQjcn/gQjWRrAlk33zibEMeFYL0GkmAO15AnBmnA8BfwhAxIXAD5aDw/YCnyXzrFOV1RlM/CVJ6jQzbOqt3mIDcKiJdxuLYGUNffYfAi6P4N5aTzyuMfk/Nqnuf2C2cwNqNk0HIDJzmgnAcCbtLbfJfBT4YwAizge+j4i1wBNUk4DTXZAGoh/YSn/+SxsiVQuY+EuS1IlmPhiqq+7DsGa9DQdqKffJJCM4AGyG2lfna0z+T0ZU5wZEcKCRjcgz3HxyB6CgegIwdQLQ3hKgyTIfB/4XABGbgBuI2AAcYTWf4Tt8HLiKgbiU/pxfeZQWJQ/wkiSpk0wkqLNc0apQpirlOBsi6K7zcyNQdfuZ5Zosh32tLavxzZM5XiYBMWkSQDkI7CiwohwE1tPU+85H5gEy/5zMjwCf5K28nVGeR9IH/Md2h6eFYeIvSVJniVlW+xdKzpaUT7GLaqNvfTeoVtRXRsw+cZloUxrBlnrvUUMQExOAiRKgiXtOnAQci24CAJB5mPfxG6zkaPnd+2ybI9ICMfGXJEkAlIS0kZKU0YhZy4f3U5XwzKlqlfmlLREf/LOID9S78n8S6K4h+T8CHIlg21zXNqQ6eXe8nAEw0wRg3aKaAPTnMeAtwC/Tn99odzhaGCb+kiR1ilk29RbrG2l9mclxYPUs79f5hOGfb4CxN0PX6xqIpdbkfxhO1f03PwHPzCkTgBWn3zpVArS4ngD052fpz19pdxhaOCb+kiR1goG4gkd4zQKW+cy5cl7H6vpfAF+Ga25tJJA6kv+Juv/1ZRNu852eADDDBOBUCdBb/ulPtr6X977rvby3bfsstLyZ+EuS1Bm20cXT23j/Osp93n9v5vtfBNcfjmBVIzerNfkv1+6j2oBbU3wNqer/J08A4vRb1QRg23U7/w3wYeD8BYtDHc12npIkdYL+/ALwhXbdPpPxeuvpMxmNYHUEI/WXC51q49kTwcm5Pp/J4Qj6Sr//xxq5X41BTbQA7aLK/ccnnsKsWjvyF8DhD/LBPQtyb3U8E39JkkQEG4GDc14439sEUWdSPQT0AYMN3vMk0E0Nm5YzGYpggKruf9+C9t4/c/U/yRz/IB8cBP56we6pjmepjyRJgiYf3DWD/cCmej5QJgljc3QNmuvzI7WWDGUyXur+N0SwtpF71qWaAGTp/29epgXlf2CSJKklMhmjgdyjdOBpqNa/fL6u5L985nGqcwHqmqg0pNoAPE41AehyAqCF4n9YkiSpKRakJ/5pwxH0NvrhBpP/Q8BQBP2N3rcus3QAkprB/6AkSepw8zi4a7KjUFNLzINlP0FdytOCqOME4OnGaCT5HwT2lsO+WrM3cpYOQNJ8mPhLkqSGDu6aLJNxaujln8kIDTYXqU71bXzVv4zRSPI/Vur+N0ewZj73r8vpCUCUPQBOADQvJv6SJGkpqStpn04jyX/53B6gp5EnFvNSTQCqiVVEV0vvrWXFxF+SJLXa4QjOaeSD83liMGWcBEYbSP6foNpvsHW+MdStSv7Hrf9Xo/yPRpIktVTp0tMzjyGGIuhrQhzjVMl/d52fOw7sL3X/rV2BP70B2A5Aqpv/sUiS1MEi2MDCH9zVVGW1frwZSXdJ/scaSP5HS93/eRGsnm8cdTu7A5D1/5qTib8kSZ2tFQd3TedoBOsb/fB8e/tPGauh5L98dgDoKxOo1jtzA3AXu+JaBqI17Ue15Jj4S5KklmtGhx7gZGlF2ox45pP8H6DaLHx+M2JpyMQEoIt3A69rWxxa1FrTj1aSJHWCwQjWlBr4BZfJWASrIohS/jPf8cYjIILusom4ns8ei2A4gguBx8pEovWCH6A/5/17oeXJFX9JkjpUkw7uOqWBjjuDEayd5z2b8eRg8ngTK/91L46Wn383cH4zNh83xKRfszDxlySpc63L5Ei7bp7JMWjKxti6O/PMpiT/2WDyn6Xuf02jLUulhWLiL0mSlrRm9fafMuYYDSb/5fP7qToPndfMuKT5MPGXJEntdKJJZTHDEc0r+YGmJP9HqQ4ruzAC222q7Uz8JUnqQCURbXsyWkqN1jVhnInynKbmNk1I/oep6v4vaPbERIqIP46IvRGxo5brTfwlSepM57LEDu6aSxNOBJ5p3DEgj31n7asYiG0NfD7LYV/r5nN2gTSNPwWur/ViE39JkjpTuw7ums5ws/rxU/XTb8rBXpNlMrZm9fGPAG+Zxxj7ACLY3LTA1NEy82bgiVqvN/GXJEnNVPehWpkchuZ0wCmTmZULUVMfwbOBD81njFLadCyCC79z21NWecquWsnEX5IkNU2z++o3aAgWoI9+f55oRp/8TE4Auzdv3P/nwGMMRNf8g9MytTkivjbp623zGcyTeyVJ6jBlk+lwu+OYYrSRE3Onk0lGMBrBykVUznSGTJKBw/vbHYcWvf2ZeU2zBnPFX5KkzrO2tJpcNDJ5AtjQxPFOQvNr/ZvsPwPn0p9j7Q5EncHEX5IkLVdN7+3fVP05Rn8ebncYWroi4i+ALwOXR8SjEfEfZrveUh9JktQ0ER/oAx6P4DWZ77+5zo+PR9BV2mfOWyZjER9+TcTx12W+/780Y0xpMcnMH6rnelf8JUnqIC04QbanfNXd7x44AGxqbjjHnwP8WHPHlJYmE39JkjrLBhbw4K7M9x8CtgJ/Wf9nmXfHnGni+RWa1CpUWuos9ZEkqbOsaFYpzUwy3//4fD4fwYpMxpsYT9PGkpYyV/wlSeoUA7HqQ+/72cV+auw+8GRbaSGY+EuS1Dn+5Gf/06/dw0AsdJ1/wxai3EdSxcRfkqTOMdjuAGrVgk3IUscx8ZckqXO8HVhHfy72VfX9WO4jNZ2Jv6a7pIYAAAFCSURBVCRJnaI/x+nPY+0OYy6ZjN/6yRc+j4G4sN2xSMuJib8kSVp0Xnjtl/8e+ES745CWExN/SZK0GH0d+Nt2ByEtJ/bxlyRJi09/PrfdIUjLjSv+kiRJUgcw8ZckSZI6gIm/JEmS1AFM/CVJkqQOYOIvSZIkdQATf0mSJKkDmPhLkiRJHcDEX5IkSeoAJv6SJElSBzDxlyRJkjqAib8kSZLUAUz8JUmSpA5g4i9JkiR1ABN/SZIkqQOY+EuSJEkdwMRfkiRJ6gAm/pIkSVIHMPGXJEmSOoCJvyRJktQBTPwlSZKkDmDiL0mSJHUAE39JkiSpA5j4S5IkSR0gMrPdMUiSJElLQkR8Ftjcotvtz8zrmzWYib8kSZLUASz1kSRJkjqAib8kSZLUAUz8JUmSpA5g4i9JkiR1ABN/SZIkqQP8/zD3RxAyJhURAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_bk = deepcopy(X)\n",
    "A_bk = deepcopy(A)\n",
    "selected_nodes = np.array([[target_idx]], dtype=\"int32\")\n",
    "nodes = [graph_nodes.index(v) for v in G_ego.nodes()]\n",
    "edges = [(graph_nodes.index(u), graph_nodes.index(v)) for u, v in G_ego.edges()]\n",
    "clean_prediction = model.predict([X, selected_nodes, A_index, A]).squeeze()\n",
    "predict_label = np.argmax(clean_prediction)\n",
    "\n",
    "groud_truth_node_importance = np.zeros((N,))\n",
    "for node in nodes:\n",
    "    # we set all the features of the node to zero to check the ground truth node importance.\n",
    "    X_perturb = deepcopy(X_bk)\n",
    "    X_perturb[:, node, :] = 0\n",
    "    predict_after_perturb = model.predict(\n",
    "        [X_perturb, selected_nodes, A_index, A]\n",
    "    ).squeeze()\n",
    "    groud_truth_node_importance[node] = (\n",
    "        clean_prediction[predict_label] - predict_after_perturb[predict_label]\n",
    "    )\n",
    "\n",
    "node_shapes = [\n",
    "    \"o\" if groud_truth_node_importance[k] > 0 else \"d\" for k in range(len(nodes))\n",
    "]\n",
    "positive_colors, negative_colors = [], []\n",
    "positive_node_sizes, negative_node_sizes = [], []\n",
    "positive_nodes, negative_nodes = [], []\n",
    "# node_size_scale is used for better visulization of nodes\n",
    "node_size_scale = node_size_factor / max(groud_truth_node_importance)\n",
    "\n",
    "for k in range(len(node_shapes)):\n",
    "    if nodes[k] == target_idx:\n",
    "        continue\n",
    "    if node_shapes[k] == \"o\":\n",
    "        positive_colors.append(colors[k])\n",
    "        positive_nodes.append(graph_nodes[nodes[k]])\n",
    "        positive_node_sizes.append(\n",
    "            node_size_scale * groud_truth_node_importance[nodes[k]]\n",
    "        )\n",
    "    else:\n",
    "        negative_colors.append(colors[k])\n",
    "        negative_nodes.append(graph_nodes[nodes[k]])\n",
    "        negative_node_sizes.append(\n",
    "            node_size_scale * abs(groud_truth_node_importance[nodes[k]])\n",
    "        )\n",
    "X = deepcopy(X_bk)\n",
    "groud_truth_edge_importance = np.zeros((N, N))\n",
    "G_edge_indices = [(A_index[0, k, 0], A_index[0, k, 1]) for k in range(A.shape[1])]\n",
    "\n",
    "for edge in edges:\n",
    "    edge_index = G_edge_indices.index((edge[0], edge[1]))\n",
    "    origin_val = A[0, edge_index]\n",
    "\n",
    "    A[0, edge_index] = 0\n",
    "    # we set the weight of a given edge to zero to check the ground truth link importance\n",
    "    predict_after_perturb = model.predict([X, selected_nodes, A_index, A]).squeeze()\n",
    "    groud_truth_edge_importance[edge[0], edge[1]] = (\n",
    "        predict_after_perturb[predict_label] - clean_prediction[predict_label]\n",
    "    ) / (0 - 1)\n",
    "    A[0, edge_index] = origin_val\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(15, 10))\n",
    "cmap = plt.get_cmap(\"jet\", np.max(colors) - np.min(colors) + 1)\n",
    "# Draw the target node as a large star colored by its true subject\n",
    "nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=[target_nid],\n",
    "    node_size=50 * abs(node_sizes[nodes.index(target_idx)]),\n",
    "    node_color=[colors[nodes.index(target_idx)]],\n",
    "    cmap=cmap,\n",
    "    node_shape=\"*\",\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    label=\"Target\",\n",
    ")\n",
    "# Draw the ego net\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=positive_nodes,\n",
    "    node_color=positive_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=positive_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"o\",\n",
    ")\n",
    "nc = nx.draw_networkx_nodes(\n",
    "    G_ego,\n",
    "    pos,\n",
    "    nodelist=negative_nodes,\n",
    "    node_color=negative_colors,\n",
    "    cmap=cmap,\n",
    "    node_size=negative_node_sizes,\n",
    "    with_labels=False,\n",
    "    vmin=np.min(colors) - 0.5,\n",
    "    vmax=np.max(colors) + 0.5,\n",
    "    node_shape=\"d\",\n",
    ")\n",
    "edges = G_ego.edges()\n",
    "weights = [\n",
    "    groud_truth_edge_importance[graph_nodes.index(u), graph_nodes.index(v)]\n",
    "    for u, v in edges\n",
    "]\n",
    "edge_colors = [\"red\" if w > 0 else \"blue\" for w in weights]\n",
    "weights = link_width_factor * np.abs(weights) / np.max(weights)\n",
    "\n",
    "ec = nx.draw_networkx_edges(G_ego, pos, edge_color=edge_colors, width=weights)\n",
    "plt.legend()\n",
    "plt.colorbar(nc, ticks=np.arange(np.min(colors), np.max(colors) + 1))\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By comparing the above two figures, one can see that the integrated gradients are quite consistent with the brute-force approach. The main benefit of using integrated gradients is scalability. The gradient operations are very efficient to compute on deep learning frameworks with the parallelism provided by GPUs. Also, integrated gradients can give the importance of individual node features, for all nodes in the graph. Achieving this by brute-force approach is often non-trivial. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "nbsphinx": "hidden",
    "tags": [
     "CloudRunner"
    ]
   },
   "source": [
    "<table><tr><td>Run the latest release of this notebook:</td><td><a href=\"https://mybinder.org/v2/gh/stellargraph/stellargraph/master?urlpath=lab/tree/demos/interpretability/gcn-sparse-node-link-importance.ipynb\" alt=\"Open In Binder\" target=\"_parent\"><img src=\"https://mybinder.org/badge_logo.svg\"/></a></td><td><a href=\"https://colab.research.google.com/github/stellargraph/stellargraph/blob/master/demos/interpretability/gcn-sparse-node-link-importance.ipynb\" alt=\"Open In Colab\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\"/></a></td></tr></table>"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
